sync

downloads/ultralytics-main/.github/ISSUE_TEMPLATE/config.yml

@@ -7,5 +7,5 @@ contact_links:
     url: https://community.ultralytics.com/
     about: Ask on Ultralytics Community Forum
   - name: 🎧 Discord
-    url: https://discord.gg/n6cFeSPZdD
+    url: https://ultralytics.com/discord
     about: Ask on Ultralytics Discord

downloads/ultralytics-main/.github/workflows/ci.yaml

@@ -10,17 +10,30 @@ on:
     branches: [main]
   schedule:
     - cron: '0 0 * * *'  # runs at 00:00 UTC every day
+  workflow_dispatch:
+    inputs:
+      hub:
+        description: 'Run HUB'
+        default: false
+        type: boolean
+      tests:
+        description: 'Run Tests'
+        default: false
+        type: boolean
+      benchmarks:
+        description: 'Run Benchmarks'
+        default: false
+        type: boolean
 
 jobs:
   HUB:
-    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push')
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && github.event.inputs.hub == 'true'))
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python-version: ['3.10']
-        model: [yolov5n]
+        python-version: ['3.11']
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
@@ -56,8 +69,26 @@ jobs:
           hub.reset_model(model_id)
           model = YOLO('https://hub.ultralytics.com/models/' + model_id)
           model.train()
+      - name: Test HUB inference API
+        shell: python
+        env:
+          API_KEY: ${{ secrets.ULTRALYTICS_HUB_API_KEY }}
+          MODEL_ID: ${{ secrets.ULTRALYTICS_HUB_MODEL_ID }}
+        run: |
+          import os
+          import requests
+          import json
+          api_key, model_id = os.environ['API_KEY'], os.environ['MODEL_ID']
+          url = f"https://api.ultralytics.com/v1/predict/{model_id}"
+          headers = {"x-api-key": api_key}
+          data = {"size": 320, "confidence": 0.25, "iou": 0.45}
+          with open("ultralytics/assets/zidane.jpg", "rb") as f:
+              response = requests.post(url, headers=headers, data=data, files={"image": f})
+          assert response.status_code == 200, f'Status code {response.status_code}, Reason {response.reason}'
+          print(json.dumps(response.json(), indent=2))
 
   Benchmarks:
+    if: github.event_name != 'workflow_dispatch' || github.event.inputs.benchmarks == 'true'
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -75,12 +106,8 @@ jobs:
         shell: bash  # for Windows compatibility
         run: |
           python -m pip install --upgrade pip wheel
-          if [ "${{ matrix.os }}" == "macos-latest" ]; then
-              pip install -e '.[export]' --extra-index-url https://download.pytorch.org/whl/cpu
-            else
-              pip install -e '.[export]' --extra-index-url https://download.pytorch.org/whl/cpu
-          fi
-          yolo export format=tflite imgsz=32
+          pip install -e ".[export]" coverage --extra-index-url https://download.pytorch.org/whl/cpu
+          yolo export format=tflite imgsz=32 || true
       - name: Check environment
         run: |
           echo "RUNNER_OS is ${{ runner.os }}"
@@ -93,44 +120,44 @@ jobs:
           pip --version
           pip list
       - name: Benchmark DetectionModel
-        shell: python
-        run: |
-          from ultralytics.yolo.utils.benchmarks import benchmark
-          benchmark(model='${{ matrix.model }}.pt', imgsz=160, half=False, hard_fail=0.20)
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.26
       - name: Benchmark SegmentationModel
-        shell: python
-        run: |
-          from ultralytics.yolo.utils.benchmarks import benchmark
-          benchmark(model='${{ matrix.model }}-seg.pt', imgsz=160, half=False, hard_fail=0.14)
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
       - name: Benchmark ClassificationModel
-        shell: python
-        run: |
-          from ultralytics.yolo.utils.benchmarks import benchmark
-          benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.61)
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.36
       - name: Benchmark PoseModel
-        shell: python
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17
+      - name: Merge Coverage Reports
         run: |
-          from ultralytics.yolo.utils.benchmarks import benchmark
-          benchmark(model='${{ matrix.model }}-pose.pt', imgsz=160, half=False, hard_fail=0.0)
+          coverage xml -o coverage-benchmarks.xml
+      - name: Upload Coverage Reports to CodeCov
+        uses: codecov/codecov-action@v3
+        with:
+          flags: Benchmarks
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
       - name: Benchmark Summary
         run: |
           cat benchmarks.log
           echo "$(cat benchmarks.log)" >> $GITHUB_STEP_SUMMARY
 
   Tests:
+    if: github.event_name != 'workflow_dispatch' || github.event.inputs.tests == 'true'
     timeout-minutes: 60
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python-version: ['3.7', '3.8', '3.9', '3.10']
-        model: [yolov8n]
+        python-version: ['3.11']
         torch: [latest]
         include:
           - os: ubuntu-latest
-            python-version: '3.8'  # torch 1.7.0 requires python >=3.6, <=3.8
-            model: yolov8n
+            python-version: '3.8'  # torch 1.8.0 requires python >=3.6, <=3.8
             torch: '1.8.0'  # min torch version CI https://pypi.org/project/torchvision/
     steps:
       - uses: actions/checkout@v3
@@ -140,12 +167,12 @@ jobs:
           cache: 'pip' # caching pip dependencies
       - name: Install requirements
         shell: bash  # for Windows compatibility
-        run: |
+        run: |  # CoreML must be installed before export due to protobuf error from AutoInstall
           python -m pip install --upgrade pip wheel
           if [ "${{ matrix.torch }}" == "1.8.0" ]; then
-              pip install -e . torch==1.8.0 torchvision==0.9.0 pytest --extra-index-url https://download.pytorch.org/whl/cpu
+              pip install -e . torch==1.8.0 torchvision==0.9.0 pytest-cov "coremltools>=7.0.b1" --extra-index-url https://download.pytorch.org/whl/cpu
           else
-              pip install -e . pytest --extra-index-url https://download.pytorch.org/whl/cpu
+              pip install -e . pytest-cov "coremltools>=7.0.b1" --extra-index-url https://download.pytorch.org/whl/cpu
           fi
       - name: Check environment
         run: |
@@ -158,41 +185,16 @@ jobs:
           python --version
           pip --version
           pip list
-      - name: Test Detect
-        shell: bash  # for Windows compatibility
-        run: |
-          yolo detect train data=coco8.yaml model=yolov8n.yaml epochs=1 imgsz=32
-          yolo detect train data=coco8.yaml model=yolov8n.pt epochs=1 imgsz=32
-          yolo detect val data=coco8.yaml model=runs/detect/train/weights/last.pt imgsz=32
-          yolo detect predict model=runs/detect/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-          yolo export model=runs/detect/train/weights/last.pt imgsz=32 format=torchscript
-      - name: Test Segment
-        shell: bash  # for Windows compatibility
-        run: |
-          yolo segment train data=coco8-seg.yaml model=yolov8n-seg.yaml epochs=1 imgsz=32
-          yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=1 imgsz=32
-          yolo segment val data=coco8-seg.yaml model=runs/segment/train/weights/last.pt imgsz=32
-          yolo segment predict model=runs/segment/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-          yolo export model=runs/segment/train/weights/last.pt imgsz=32 format=torchscript
-      - name: Test Classify
-        shell: bash  # for Windows compatibility
-        run: |
-          yolo classify train data=imagenet10 model=yolov8n-cls.yaml epochs=1 imgsz=32
-          yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=1 imgsz=32
-          yolo classify val data=imagenet10 model=runs/classify/train/weights/last.pt imgsz=32
-          yolo classify predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-          yolo export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript
-      - name: Test Pose
-        shell: bash  # for Windows compatibility
-        run: |
-          yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml epochs=1 imgsz=32
-          yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=1 imgsz=32
-          yolo pose val data=coco8-pose.yaml model=runs/pose/train/weights/last.pt imgsz=32
-          yolo pose predict model=runs/pose/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-          yolo export model=runs/pose/train/weights/last.pt imgsz=32 format=torchscript
       - name: Pytest tests
         shell: bash  # for Windows compatibility
-        run: pytest tests
+        run: pytest --cov=ultralytics/ --cov-report xml tests/
+      - name: Upload Coverage Reports to CodeCov
+        if: github.repository == 'ultralytics/ultralytics' && matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
+        uses: codecov/codecov-action@v3
+        with:
+          flags: Tests
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
   Summary:
     runs-on: ubuntu-latest
@@ -201,7 +203,7 @@ jobs:
     steps:
       - name: Check for failure and notify
         if: (needs.HUB.result == 'failure' || needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure') && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push')
-        uses: slackapi/slack-github-action@v1.23.0
+        uses: slackapi/slack-github-action@v1.24.0
         with:
           payload: |
             {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"}

downloads/ultralytics-main/.github/workflows/docker.yaml

@@ -6,12 +6,47 @@ name: Publish Docker Images
 on:
   push:
     branches: [main]
+  workflow_dispatch:
+    inputs:
+      dockerfile:
+        type: choice
+        description: Select Dockerfile
+        options:
+          - Dockerfile-arm64
+          - Dockerfile-jetson
+          - Dockerfile-python
+          - Dockerfile-cpu
+          - Dockerfile
+      push:
+        type: boolean
+        description: Push image to Docker Hub
+        default: true
 
 jobs:
   docker:
     if: github.repository == 'ultralytics/ultralytics'
-    name: Push Docker image to Docker Hub
+    name: Push
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      max-parallel: 5
+      matrix:
+        include:
+          - dockerfile: "Dockerfile-arm64"
+            tags: "latest-arm64"
+            platforms: "linux/arm64"
+          - dockerfile: "Dockerfile-jetson"
+            tags: "latest-jetson"
+            platforms: "linux/arm64"
+          - dockerfile: "Dockerfile-python"
+            tags: "latest-python"
+            platforms: "linux/amd64"
+          - dockerfile: "Dockerfile-cpu"
+            tags: "latest-cpu"
+            platforms: "linux/amd64"
+          - dockerfile: "Dockerfile"
+            tags: "latest"
+            platforms: "linux/amd64"
     steps:
       - name: Checkout repo
         uses: actions/checkout@v3
@@ -28,40 +63,66 @@ jobs:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
 
-      - name: Build and push arm64 image
-        uses: docker/build-push-action@v4
-        continue-on-error: true
-        with:
-          context: .
-          platforms: linux/arm64
-          file: docker/Dockerfile-arm64
-          push: true
-          tags: ultralytics/ultralytics:latest-arm64
+      - name: Retrieve Ultralytics version
+        id: get_version
+        run: |
+          VERSION=$(grep "^__version__ =" ultralytics/__init__.py | awk -F"'" '{print $2}')
+          echo "Retrieved Ultralytics version: $VERSION"
+          echo "version=$VERSION" >> $GITHUB_OUTPUT
 
-      - name: Build and push Jetson image
-        uses: docker/build-push-action@v4
-        continue-on-error: true
-        with:
-          context: .
-          platforms: linux/arm64
-          file: docker/Dockerfile-jetson
-          push: true
-          tags: ultralytics/ultralytics:latest-jetson
+          VERSION_TAG=$(echo "${{ matrix.tags }}" | sed "s/latest/${VERSION}/")
+          echo "Intended version tag: $VERSION_TAG"
+          echo "version_tag=$VERSION_TAG" >> $GITHUB_OUTPUT
 
-      - name: Build and push CPU image
-        uses: docker/build-push-action@v4
-        continue-on-error: true
-        with:
-          context: .
-          file: docker/Dockerfile-cpu
-          push: true
-          tags: ultralytics/ultralytics:latest-cpu
+      - name: Check if version tag exists on DockerHub
+        id: check_tag
+        run: |
+          RESPONSE=$(curl -s https://hub.docker.com/v2/repositories/ultralytics/ultralytics/tags/$VERSION_TAG)
+          MESSAGE=$(echo $RESPONSE | jq -r '.message')
+          if [[ "$MESSAGE" == "null" ]]; then
+              echo "Tag $VERSION_TAG already exists on DockerHub."
+              echo "exists=true" >> $GITHUB_OUTPUT
+          elif [[ "$MESSAGE" == *"404"* ]]; then
+              echo "Tag $VERSION_TAG does not exist on DockerHub."
+              echo "exists=false" >> $GITHUB_OUTPUT
+          else
+              echo "Unexpected response from DockerHub. Please check manually."
+              echo "exists=false" >> $GITHUB_OUTPUT
+          fi
+        env:
+          VERSION_TAG: ${{ steps.get_version.outputs.version_tag }}
 
-      - name: Build and push GPU image
-        uses: docker/build-push-action@v4
-        continue-on-error: true
+      - name: Build Image
+        if: github.event_name == 'push' || github.event.inputs.dockerfile == matrix.dockerfile
+        run: |
+          docker build --platform ${{ matrix.platforms }} -f docker/${{ matrix.dockerfile }} \
+          -t ultralytics/ultralytics:${{ matrix.tags }} \
+          -t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} .
+
+      - name: Run Tests
+        if: (github.event_name == 'push' || github.event.inputs.dockerfile == matrix.dockerfile) && matrix.platforms == 'linux/amd64'  # arm64 images not supported on GitHub CI runners
+        run: docker run ultralytics/ultralytics:${{ matrix.tags }} /bin/bash -c "pip install pytest && pytest tests"
+
+      - name: Run Benchmarks
+        # WARNING: Dockerfile (GPU) error on TF.js export 'module 'numpy' has no attribute 'object'.
+        if: (github.event_name == 'push' || github.event.inputs.dockerfile == matrix.dockerfile) && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile'  # arm64 images not supported on GitHub CI runners
+        run: docker run ultralytics/ultralytics:${{ matrix.tags }} yolo benchmark model=yolov8n.pt imgsz=160 verbose=0.26
+
+      - name: Push Docker Image with Ultralytics version tag
+        if: (github.event_name == 'push' || (github.event.inputs.dockerfile == matrix.dockerfile && github.event.inputs.push == 'true')) && steps.check_tag.outputs.exists == 'false'
+        run: |
+          docker push ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }}
+
+      - name: Push Docker Image with latest tag
+        if: github.event_name == 'push' || (github.event.inputs.dockerfile == matrix.dockerfile && github.event.inputs.push == 'true')
+        run: |
+          docker push ultralytics/ultralytics:${{ matrix.tags }}
+
+      - name: Notify on failure
+        if: github.event_name == 'push' && failure()  # do not notify on cancelled() as cancelling is performed by hand
+        uses: slackapi/slack-github-action@v1.24.0
         with:
-          context: .
-          file: docker/Dockerfile
-          push: true
-          tags: ultralytics/ultralytics:latest
+          payload: |
+            {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"}
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}

downloads/ultralytics-main/.github/workflows/greetings.yml

@@ -32,9 +32,11 @@ jobs:
 
             If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/).
 
+            Join the vibrant [Ultralytics Discord](https://ultralytics.com/discord) 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
+
             ## Install
 
-            Pip install the `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+            Pip install the `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
 
             ```bash
             pip install ultralytics

downloads/ultralytics-main/.github/workflows/links.yml

@@ -28,7 +28,7 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
+          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
 
       - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
         if: github.event_name == 'workflow_dispatch'
@@ -37,4 +37,4 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
+          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'

downloads/ultralytics-main/.github/workflows/publish.yml

@@ -33,14 +33,14 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip wheel build twine
-          pip install -e '.[dev]' --extra-index-url https://download.pytorch.org/whl/cpu
+          pip install -e ".[dev]" --extra-index-url https://download.pytorch.org/whl/cpu
       - name: Check PyPI version
         shell: python
         run: |
           import os
           import pkg_resources as pkg
           import ultralytics
-          from ultralytics.yolo.utils.checks import check_latest_pypi_version
+          from ultralytics.utils.checks import check_latest_pypi_version
 
           v_local = pkg.parse_version(ultralytics.__version__).release
           v_pypi = pkg.parse_version(check_latest_pypi_version()).release
@@ -63,7 +63,7 @@ jobs:
           python -m twine upload dist/* -u __token__ -p $PYPI_TOKEN
       - name: Deploy Docs
         continue-on-error: true
-        if: (github.event_name == 'push' && steps.check_pypi.outputs.increment == 'True') || github.event.inputs.docs == 'true'
+        if: (github.event_name == 'push' || github.event.inputs.docs == 'true') && github.repository == 'ultralytics/ultralytics' && github.actor == 'glenn-jocher'
         env:
           PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
         run: |
@@ -94,7 +94,7 @@ jobs:
           echo "PR_TITLE=$PR_TITLE" >> $GITHUB_ENV
       - name: Notify on Slack (Success)
         if: success() && github.event_name == 'push' && steps.check_pypi.outputs.increment == 'True'
-        uses: slackapi/slack-github-action@v1.23.0
+        uses: slackapi/slack-github-action@v1.24.0
         with:
           payload: |
             {"text": "<!channel> GitHub Actions success for ${{ github.workflow }} ✅\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* NEW 'ultralytics ${{ steps.check_pypi.outputs.version }}' pip package published 😃\n*Job Status:* ${{ job.status }}\n*Pull Request:* <https://github.com/${{ github.repository }}/pull/${{ env.PR_NUMBER }}> ${{ env.PR_TITLE }}\n"}
@@ -102,7 +102,7 @@ jobs:
           SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
       - name: Notify on Slack (Failure)
         if: failure()
-        uses: slackapi/slack-github-action@v1.23.0
+        uses: slackapi/slack-github-action@v1.24.0
         with:
           payload: |
             {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n*Job Status:* ${{ job.status }}\n*Pull Request:* <https://github.com/${{ github.repository }}/pull/${{ env.PR_NUMBER }}> ${{ env.PR_TITLE }}\n"}

downloads/ultralytics-main/.gitignore

@@ -118,11 +118,15 @@ venv.bak/
 .spyderproject
 .spyproject
 
+# VSCode project settings
+.vscode/
+
 # Rope project settings
 .ropeproject
 
 # mkdocs documentation
 /site
+mkdocs_github_authors.yaml
 
 # mypy
 .mypy_cache/
@@ -136,7 +140,6 @@ dmypy.json
 datasets/
 runs/
 wandb/
-
 .DS_Store
 
 # Neural Network weights -----------------------------------------------------------------------------------------------
@@ -147,6 +150,7 @@ weights/
 *.onnx
 *.engine
 *.mlmodel
+*.mlpackage
 *.torchscript
 *.tflite
 *.h5
@@ -154,3 +158,6 @@ weights/
 *_web_model/
 *_openvino_model/
 *_paddle_model/
+
+# Autogenerated files for tests
+/ultralytics/assets/

downloads/ultralytics-main/.pre-commit-config.yaml

@@ -22,7 +22,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.2
+    rev: v3.10.1
     hooks:
       - id: pyupgrade
         name: Upgrade code
@@ -34,7 +34,7 @@ repos:
         name: Sort imports
 
   - repo: https://github.com/google/yapf
-    rev: v0.33.0
+    rev: v0.40.0
     hooks:
       - id: yapf
         name: YAPF formatting
@@ -50,17 +50,17 @@ repos:
         # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 6.1.0
     hooks:
       - id: flake8
         name: PEP8
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.4
+    rev: v2.2.5
     hooks:
       - id: codespell
         args:
-          - --ignore-words-list=crate,nd,strack,dota
+          - --ignore-words-list=crate,nd,strack,dota,ane,segway,fo
 
 #  - repo: https://github.com/asottile/yesqa
 #    rev: v1.4.0

downloads/ultralytics-main/CONTRIBUTING.md

@@ -65,7 +65,7 @@ Here is an example:
 
 ```python
 """
-   What the function does. Performs NMS on given detection predictions.
+    What the function does. Performs NMS on given detection predictions.
 
     Args:
         arg1: The description of the 1st argument

downloads/ultralytics-main/README.md

@@ -9,6 +9,7 @@
 
 <div>
     <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
     <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
     <br>
@@ -20,7 +21,7 @@
 
 [Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
 
-We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.gg/n6cFeSPZdD">Discord</a> community for questions and discussions!
+We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
 
 To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
 
@@ -30,7 +31,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@@ -45,7 +46,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://ultralytics.com/discord" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
 </div>
 </div>
@@ -57,12 +58,16 @@ See below for a quickstart installation and usage example, and see the [YOLOv8 D
 <details open>
 <summary>Install</summary>
 
-Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
 
 ```bash
 pip install ultralytics
 ```
 
+For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
+
 </details>
 
 <details open>
@@ -93,18 +98,20 @@ model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 model.train(data="coco128.yaml", epochs=3)  # train the model
 metrics = model.val()  # evaluate model performance on the validation set
 results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
-success = model.export(format="onnx")  # export the model to ONNX format
+path = model.export(format="onnx")  # export the model to ONNX format
 ```
 
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
 
 </details>
 
 ## <div align="center">Models</div>
 
-All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
 
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
+
+All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 
 <details open><summary>Detection</summary>
 
@@ -186,6 +193,8 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose) for usage examples with
 
 ## <div align="center">Integrations</div>
 
+Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow.
+
 <br>
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
@@ -228,21 +237,21 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou
 
 ## <div align="center">License</div>
 
-YOLOv8 is available under two different licenses:
+Ultralytics offers two licensing options to accommodate diverse use cases:
 
-- **AGPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
-- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
+- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license).
 
 ## <div align="center">Contact</div>
 
-For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://discord.gg/n6cFeSPZdD) community for questions and discussions!
+For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://ultralytics.com/discord) community for questions and discussions!
 
 <br>
 <div align="center">
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@@ -257,6 +266,6 @@ For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https:/
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://ultralytics.com/discord" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
 </div>

downloads/ultralytics-main/README.zh-CN.md

@@ -9,6 +9,7 @@
 
 <div>
     <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
     <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
     <br>
@@ -20,7 +21,7 @@
 
 [Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。
 
-我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 <a href="https://docs.ultralytics.com/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://discord.gg/n6cFeSPZdD">Discord</a> 社区进行问题和讨论！
+我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 <a href="https://docs.ultralytics.com/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://ultralytics.com/discord">Discord</a> 社区进行问题和讨论！
 
 如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格
 
@@ -30,7 +31,7 @@
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@@ -45,7 +46,7 @@
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://ultralytics.com/discord" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
 </div>
 </div>
@@ -57,12 +58,16 @@
 <details open>
 <summary>安装</summary>
 
-在一个 [**Python>=3.7**](https://www.python.org/) 环境中，使用 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)，通过 pip 安装 ultralytics 软件包以及所有[依赖项](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt)。
+使用Pip在一个[**Python>=3.8**](https://www.python.org/)环境中安装`ultralytics`包，此环境还需包含[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。这也会安装所有必要的[依赖项](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt)。
+
+[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
 
 ```bash
 pip install ultralytics
 ```
 
+如需使用包括[Conda](https://anaconda.org/conda-forge/ultralytics)、[Docker](https://hub.docker.com/r/ultralytics/ultralytics)和Git在内的其他安装方法，请参考[快速入门指南](https://docs.ultralytics.com/quickstart)。
+
 </details>
 
 <details open>
@@ -96,15 +101,17 @@ results = model("https://ultralytics.com/images/bus.jpg")  # 对图像进行预
 success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 ```
 
-[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。查看 YOLOv8 [Python 文档](https://docs.ultralytics.com/usage/python)以获取更多示例。
+[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) 会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。查看 YOLOv8 [Python 文档](https://docs.ultralytics.com/usage/python)以获取更多示例。
 
 </details>
 
 ## <div align="center">模型</div>
 
-所有的 YOLOv8 预训练模型都可以在此找到。检测、分割和姿态模型在 [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) 数据集上进行预训练，而分类模型在 [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) 数据集上进行预训练。
+在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
 
-在首次使用时，[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
+
+所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时会自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
 
 <details open><summary>检测</summary>
 
@@ -185,6 +192,8 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 ## <div align="center">集成</div>
 
+我们与领先的AI平台的关键整合扩展了Ultralytics产品的功能，增强了数据集标签化、训练、可视化和模型管理等任务。探索Ultralytics如何与[Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic以及[OpenVINO](https://docs.ultralytics.com/integrations/openvino)合作，优化您的AI工作流程。
+
 <br>
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
@@ -227,21 +236,21 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 ## <div align="center">许可证</div>
 
-YOLOv8 提供两种不同的许可证：
+Ultralytics 提供两种许可证选项以适应各种使用场景：
 
-- **AGPL-3.0 许可证**：详细信息请参阅 [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件。
-- **企业许可证**：为商业产品开发提供更大的灵活性，无需遵循 AGPL-3.0 的开源要求。典型的用例是将 Ultralytics 软件和 AI 模型嵌入商业产品和应用中。在 [Ultralytics 授权](https://ultralytics.com/license) 处申请企业许可证。
+- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件以了解更多细节。
+- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://ultralytics.com/license)与我们联系。
 
 ## <div align="center">联系方式</div>
 
-对于 YOLOv8 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)，并加入我们的 [Discord](https://discord.gg/n6cFeSPZdD) 社区进行问题和讨论！
+对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)，并加入我们的 [Discord](https://ultralytics.com/discord) 社区进行问题和讨论！
 
 <br>
 <div align="center">
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@@ -255,6 +264,6 @@ YOLOv8 提供两种不同的许可证：
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://ultralytics.com/discord" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
 </div>

downloads/ultralytics-main/docker/Dockerfile

@@ -3,15 +3,16 @@
 # Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
 
 # Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3
-FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+RUN pip install --no-cache nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
-# g++ required to build 'tflite_support' package
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
 RUN apt update \
-    && apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
+    && apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
 # RUN alias python=python3
 
 # Security updates
@@ -19,7 +20,6 @@ RUN apt update \
 RUN apt upgrade --no-install-recommends -y openssl tar
 
 # Create working directory
-RUN mkdir -p /usr/src/ultralytics
 WORKDIR /usr/src/ultralytics
 
 # Copy contents
@@ -29,10 +29,22 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e . albumentations comet tensorboard
+RUN pip install --no-cache -e ".[export]" thop albumentations comet pycocotools
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0
+RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
+# Fix error: `np.bool` was a deprecated alias for the builtin `bool`
+RUN pip install --no-cache numpy==1.23.5
+# Remove exported models
+RUN rm -rf tmp
 
 # Set environment variables
 ENV OMP_NUM_THREADS=1
+# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library" https://github.com/pytorch/pytorch/issues/37377
+ENV MKL_THREADING_LAYER=GNU
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------

downloads/ultralytics-main/docker/Dockerfile-arm64

@@ -9,12 +9,12 @@ FROM arm64v8/ubuntu:22.10
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
 RUN apt update \
-    && apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev
+    && apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
 # RUN alias python=python3
 
 # Create working directory
-RUN mkdir -p /usr/src/ultralytics
 WORKDIR /usr/src/ultralytics
 
 # Copy contents
@@ -24,7 +24,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e .
+RUN pip install --no-cache -e . thop
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------
@@ -32,5 +32,8 @@ RUN pip install --no-cache -e .
 # Build and Push
 # t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-arm64 -t $t . && sudo docker push $t
 
-# Pull and Run
+# Run
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
 # t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

downloads/ultralytics-main/docker/Dockerfile-cpu

@@ -3,19 +3,18 @@
 # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:22.10
+FROM ubuntu:lunar-20230615
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
-# g++ required to build 'tflite_support' package
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
 RUN apt update \
-    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
 # RUN alias python=python3
 
 # Create working directory
-RUN mkdir -p /usr/src/ultralytics
 WORKDIR /usr/src/ultralytics
 
 # Copy contents
@@ -23,15 +22,28 @@ WORKDIR /usr/src/ultralytics
 RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
 ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
 
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e . --extra-index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache -e ".[export]" thop --extra-index-url https://download.pytorch.org/whl/cpu
 
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0
+# RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
+# Remove exported models
+RUN rm -rf tmp
 
 # Usage Examples -------------------------------------------------------------------------------------------------------
 
 # Build and Push
 # t=ultralytics/ultralytics:latest-cpu && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
 
-# Pull and Run
+# Run
+# t=ultralytics/ultralytics:latest-cpu && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
 # t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

downloads/ultralytics-main/docker/Dockerfile-jetson

@@ -9,13 +9,12 @@ FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
-# g++ required to build 'tflite_support' package
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
 RUN apt update \
-    && apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
+    && apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
 # RUN alias python=python3
 
 # Create working directory
-RUN mkdir -p /usr/src/ultralytics
 WORKDIR /usr/src/ultralytics
 
 # Copy contents
@@ -23,9 +22,12 @@ WORKDIR /usr/src/ultralytics
 RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
 ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
 
+# Remove opencv-python from requirements.txt as it conflicts with opencv-python installed in base image
+RUN grep -v '^opencv-python' requirements.txt > tmp.txt && mv tmp.txt requirements.txt
+
 # Install pip packages manually for TensorRT compatibility https://github.com/NVIDIA/TensorRT/issues/2567
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache tqdm matplotlib pyyaml psutil pandas onnx "numpy==1.23"
+RUN pip install --no-cache tqdm matplotlib pyyaml psutil pandas onnx thop "numpy==1.23"
 RUN pip install --no-cache -e .
 
 # Set environment variables
@@ -37,5 +39,8 @@ ENV OMP_NUM_THREADS=1
 # Build and Push
 # t=ultralytics/ultralytics:latest-jetson && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson -t $t . && sudo docker push $t
 
-# Pull and Run
-# t=ultralytics/ultralytics:jetson && sudo docker pull $t && sudo docker run -it --runtime=nvidia $t
+# Run
+# t=ultralytics/ultralytics:latest-jetson && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

downloads/ultralytics-main/docker/Dockerfile-python

@@ -0,0 +1,49 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
+
+# Use the official Python 3.10 slim-bookworm as base image
+FROM python:3.10-slim-bookworm
+
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/app  (issues as not a .git directory)
+RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
+
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+# RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]" thop --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0
+RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
+# Remove exported models
+RUN rm -rf tmp
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-python && sudo docker build -f docker/Dockerfile-python -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-python && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

downloads/ultralytics-main/docs/CNAME

@@ -1 +1 @@
-docs.ultralytics.com
+docs.ultralytics.com

downloads/ultralytics-main/docs/README.md

@@ -1,5 +1,6 @@
 ---
-description: Learn how to install the Ultralytics package in developer mode and build/serve locally using MkDocs. Deploy your project to your host easily.
+description: Learn how to install Ultralytics in developer mode, build and serve it locally for testing, and deploy your documentation site on platforms like GitHub Pages, GitLab Pages, and Amazon S3.
+keywords: Ultralytics, documentation, mkdocs, installation, developer mode, building, deployment, local server, GitHub Pages, GitLab Pages, Amazon S3
 ---
 
 # Ultralytics Docs
@@ -26,7 +27,7 @@ cd ultralytics
 3. Install the package in developer mode using pip:
 
 ```bash
-pip install -e '.[dev]'
+pip install -e ".[dev]"
 ```
 
 This will install the ultralytics package and its dependencies in developer mode, allowing you to make changes to the
@@ -86,4 +87,4 @@ for your repository and updating the "Custom domain" field in the "GitHub Pages"
 ![196814117-fc16e711-d2be-4722-9536-b7c6d78fd167](https://user-images.githubusercontent.com/26833433/210150206-9e86dcd7-10af-43e4-9eb2-9518b3799eac.png)
 
 For more information on deploying your MkDocs documentation site, see
-the [MkDocs documentation](https://www.mkdocs.org/user-guide/deploying-your-docs/).
+the [MkDocs documentation](https://www.mkdocs.org/user-guide/deploying-your-docs/).

downloads/ultralytics-main/docs/SECURITY.md

@@ -1,32 +1,26 @@
 ---
-description: Learn how Ultralytics prioritize security. Get insights into Snyk and GitHub CodeQL scans, and how to report security issues in YOLOv8.
+description: Discover how Ultralytics ensures the safety of user data and systems. Check out the measures we have implemented, including Snyk and GitHub CodeQL Scanning.
+keywords: Ultralytics, Security Policy, data security, open-source projects, Snyk scanning, CodeQL scanning, vulnerability detection, threat prevention
 ---
 
 # Security Policy
 
-At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To
-ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented
-several measures to detect and prevent security vulnerabilities.
-
-[![ultralytics](https://snyk.io/advisor/python/ultralytics/badge.svg)](https://snyk.io/advisor/python/ultralytics)
+At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities.
 
 ## Snyk Scanning
 
-We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan the YOLOv8 repository for vulnerabilities
-and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any
-risks to our users.
+We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan all Ultralytics repositories for vulnerabilities and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any risks to our users.
+
+[![ultralytics](https://snyk.io/advisor/python/ultralytics/badge.svg)](https://snyk.io/advisor/python/ultralytics)
 
 ## GitHub CodeQL Scanning
 
-In addition to our Snyk scans, we also use
-GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql)
-scans to proactively identify and address security vulnerabilities.
+In addition to our Snyk scans, we also use GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) scans to proactively identify and address security vulnerabilities across all Ultralytics repositories.
+
+[![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml)
 
 ## Reporting Security Issues
 
-If you suspect or discover a security vulnerability in the YOLOv8 repository, please let us know immediately. You can
-reach out to us directly via our [contact form](https://ultralytics.com/contact) or
-via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon
-as possible.
+If you suspect or discover a security vulnerability in any of our repositories, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible.
 
-We appreciate your help in keeping the YOLOv8 repository secure and safe for everyone.
+We appreciate your help in keeping all Ultralytics open-source projects secure and safe for everyone.

downloads/ultralytics-main/docs/build_reference.py

@@ -10,7 +10,8 @@ import os
 import re
 from collections import defaultdict
 from pathlib import Path
-from ultralytics.yolo.utils import ROOT
+
+from ultralytics.utils import ROOT
 
 NEW_YAML_DIR = ROOT.parent
 CODE_DIR = ROOT
@@ -21,8 +22,8 @@ def extract_classes_and_functions(filepath):
     with open(filepath, 'r') as file:
         content = file.read()
 
-    class_pattern = r"(?:^|\n)class\s(\w+)(?:\(|:)"
-    func_pattern = r"(?:^|\n)def\s(\w+)\("
+    class_pattern = r'(?:^|\n)class\s(\w+)(?:\(|:)'
+    func_pattern = r'(?:^|\n)def\s(\w+)\('
 
     classes = re.findall(class_pattern, content)
     functions = re.findall(func_pattern, content)
@@ -34,17 +35,26 @@ def create_markdown(py_filepath, module_path, classes, functions):
     md_filepath = py_filepath.with_suffix('.md')
 
     # Read existing content and keep header content between first two ---
-    header_content = ""
+    header_content = ''
     if md_filepath.exists():
         with open(md_filepath, 'r') as file:
             existing_content = file.read()
-            header_parts = existing_content.split('---', 2)
-            if len(header_parts) >= 3:
-                header_content = f"{header_parts[0]}---{header_parts[1]}---\n\n"
+            header_parts = existing_content.split('---')
+            for part in header_parts:
+                if 'description:' in part or 'comments:' in part:
+                    header_content += f'---{part}---\n\n'
 
-    md_content = [f"# {class_name}\n---\n:::{module_path}.{class_name}\n<br><br>\n" for class_name in classes]
-    md_content.extend(f"# {func_name}\n---\n:::{module_path}.{func_name}\n<br><br>\n" for func_name in functions)
-    md_content = header_content + "\n".join(md_content)
+    module_name = module_path.replace('.__init__', '')
+    module_path = module_path.replace(".", "/")
+    url = f'https://github.com/ultralytics/ultralytics/blob/main/{module_path}.py'
+    title_content = (f'# Reference for `{module_path}.py`\n\n'
+                     f'!!! note\n\n'
+                     f'    Full source code for this file is available at [{url}]({url}). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!\n\n')
+    md_content = [f'---\n## ::: {module_name}.{class_name}\n<br><br>\n' for class_name in classes]
+    md_content.extend(f'---\n## ::: {module_name}.{func_name}\n<br><br>\n' for func_name in functions)
+    md_content = header_content + title_content + '\n'.join(md_content)
+    if not md_content.endswith('\n'):
+        md_content += '\n'
 
     os.makedirs(os.path.dirname(md_filepath), exist_ok=True)
     with open(md_filepath, 'w') as file:
@@ -80,11 +90,11 @@ def create_nav_menu_yaml(nav_items):
     nav_tree_sorted = sort_nested_dict(nav_tree)
 
     def _dict_to_yaml(d, level=0):
-        yaml_str = ""
-        indent = "  " * level
+        yaml_str = ''
+        indent = '  ' * level
         for k, v in d.items():
             if isinstance(v, dict):
-                yaml_str += f"{indent}- {k}:\n{_dict_to_yaml(v, level + 1)}"
+                yaml_str += f'{indent}- {k}:\n{_dict_to_yaml(v, level + 1)}'
             else:
                 yaml_str += f"{indent}- {k}: {str(v).replace('docs/', '')}\n"
         return yaml_str
@@ -98,7 +108,7 @@ def main():
     nav_items = []
     for root, _, files in os.walk(CODE_DIR):
         for file in files:
-            if file.endswith(".py") and file != "__init__.py":
+            if file.endswith('.py'):
                 py_filepath = Path(root) / file
                 classes, functions = extract_classes_and_functions(py_filepath)
 
@@ -112,5 +122,5 @@ def main():
     create_nav_menu_yaml(nav_items)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()

downloads/ultralytics-main/docs/guides/index.md

@@ -0,0 +1,19 @@
+---
+comments: true
+description: In-depth exploration of Ultralytics' YOLO. Learn about the YOLO object detection model, how to train it on custom data, multi-GPU training, exporting, predicting, deploying, and more.
+keywords: Ultralytics, YOLO, Deep Learning, Object detection, PyTorch, Tutorial, Multi-GPU training, Custom data training
+---
+
+# Comprehensive Tutorials to Ultralytics YOLO
+
+Welcome to the Ultralytics' YOLO 🚀 Guides! Our comprehensive tutorials cover various aspects of the YOLO object detection model, ranging from training and prediction to deployment. Built on PyTorch, YOLO stands out for its exceptional speed and accuracy in real-time object detection tasks.
+
+Whether you're a beginner or an expert in deep learning, our tutorials offer valuable insights into the implementation and optimization of YOLO for your computer vision projects. Let's dive in!
+
+## Guides
+
+Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO.
+
+* [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique.
+
+Note: More guides about training, exporting, predicting, and deploying with Ultralytics YOLO are coming soon. Stay tuned!

downloads/ultralytics-main/docs/guides/kfold-cross-validation.md

@@ -0,0 +1,265 @@
+---
+comments: true
+description: An in-depth guide demonstrating the implementation of K-Fold Cross Validation with the Ultralytics ecosystem for object detection datasets, leveraging Python, YOLO, and sklearn.
+keywords: K-Fold cross validation, Ultralytics, YOLO detection format, Python, sklearn, object detection
+---
+
+# K-Fold Cross Validation with Ultralytics
+
+## Introduction
+
+This comprehensive guide illustrates the implementation of K-Fold Cross Validation for object detection datasets within the Ultralytics ecosystem. We'll leverage the YOLO detection format and key Python libraries such as sklearn, pandas, and PyYaml to guide you through the necessary setup, the process of generating feature vectors, and the execution of a K-Fold dataset split.
+
+<p align="center">
+  <img width="800" src="https://user-images.githubusercontent.com/26833433/258589390-8d815058-ece8-48b9-a94e-0e1ab53ea0f6.png" alt="K-Fold Cross Validation Overview">
+</p>
+
+Whether your project involves the Fruit Detection dataset or a custom data source, this tutorial aims to help you comprehend and apply K-Fold Cross Validation to bolster the reliability and robustness of your machine learning models. While we're applying `k=5` folds for this tutorial, keep in mind that the optimal number of folds can vary depending on your dataset and the specifics of your project.
+
+Without further ado, let's dive in!
+
+## Setup
+
+- Your annotations should be in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
+
+- This guide assumes that annotation files are locally available.
+
+    - For our demonstration, we use the [Fruit Detection](https://www.kaggle.com/datasets/lakshaytyagi01/fruit-detection/code) dataset.
+
+        - This dataset contains a total of 8479 images.
+        - It includes 6 class labels, each with its total instance counts listed below.
+
+          | Class Label | Instance Count |
+          |:------------|:--------------:|
+          | Apple       |      7049      |
+          | Grapes      |      7202      |
+          | Pineapple   |      1613      |
+          | Orange      |     15549      |
+          | Banana      |      3536      |
+          | Watermelon  |      1976      |
+
+- Necessary Python packages include:
+
+    - `ultralytics`
+    - `sklearn`
+    - `pandas`
+    - `pyyaml`
+
+- This tutorial operates with `k=5` folds. However, you should determine the best number of folds for your specific dataset.
+
+1. Initiate a new Python virtual environment (`venv`) for your project and activate it. Use `pip` (or your preferred package manager) to install:
+
+    - The Ultralytics library: `pip install -U ultralytics`. Alternatively, you can clone the official [repo](https://github.com/ultralytics/ultralytics).
+    - Scikit-learn, pandas, and PyYAML: `pip install -U scikit-learn pandas pyyaml`.
+
+2. Verify that your annotations are in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
+
+    - For this tutorial, all annotation files are found in the `Fruit-Detection/labels` directory.
+
+## Generating Feature Vectors for Object Detection Dataset
+
+1. Start by creating a new Python file and import the required libraries.
+
+    ```python
+    import datetime
+    import shutil
+    from pathlib import Path
+    from collections import Counter
+
+    import yaml
+    import numpy as np
+    import pandas as pd
+    from ultralytics import YOLO
+    from sklearn.model_selection import KFold
+    ```
+
+2. Proceed to retrieve all label files for your dataset.
+
+    ```python
+    dataset_path = Path('./Fruit-detection') # replace with 'path/to/dataset' for your custom data
+    labels = sorted(dataset_path.rglob("*labels/*.txt")) # all data in 'labels'
+    ```
+
+3. Now, read the contents of the dataset YAML file and extract the indices of the class labels.
+
+    ```python
+    with open(yaml_file, 'r', encoding="utf8") as y:
+        classes = yaml.safe_load(y)['names']
+    cls_idx = sorted(classes.keys())
+    ```
+
+4. Initialize an empty `pandas` DataFrame.
+
+    ```python
+    indx = [l.stem for l in labels] # uses base filename as ID (no extension)
+    labels_df = pd.DataFrame([], columns=cls_idx, index=indx)
+    ```
+
+5. Count the instances of each class-label present in the annotation files.
+
+    ```python
+    for label in labels:
+        lbl_counter = Counter()
+
+        with open(label,'r') as lf:
+            lines = lf.readlines()
+
+        for l in lines:
+            # classes for YOLO label uses integer at first position of each line
+            lbl_counter[int(l.split(' ')[0])] += 1
+
+        labels_df.loc[label.stem] = lbl_counter
+
+    labels_df = labels_df.fillna(0.0) # replace `nan` values with `0.0`
+    ```
+
+6. The following is a sample view of the populated DataFrame:
+
+    ```pandas
+                                                           0    1    2    3    4    5
+    '0000a16e4b057580_jpg.rf.00ab48988370f64f5ca8ea4...'  0.0  0.0  0.0  0.0  0.0  7.0
+    '0000a16e4b057580_jpg.rf.7e6dce029fb67f01eb19aa7...'  0.0  0.0  0.0  0.0  0.0  7.0
+    '0000a16e4b057580_jpg.rf.bc4d31cdcbe229dd022957a...'  0.0  0.0  0.0  0.0  0.0  7.0
+    '00020ebf74c4881c_jpg.rf.508192a0a97aa6c4a3b6882...'  0.0  0.0  0.0  1.0  0.0  0.0
+    '00020ebf74c4881c_jpg.rf.5af192a2254c8ecc4188a25...'  0.0  0.0  0.0  1.0  0.0  0.0
+     ...                                                  ...  ...  ...  ...  ...  ...
+    'ff4cd45896de38be_jpg.rf.c4b5e967ca10c7ced3b9e97...'  0.0  0.0  0.0  0.0  0.0  2.0
+    'ff4cd45896de38be_jpg.rf.ea4c1d37d2884b3e3cbce08...'  0.0  0.0  0.0  0.0  0.0  2.0
+    'ff5fd9c3c624b7dc_jpg.rf.bb519feaa36fc4bf630a033...'  1.0  0.0  0.0  0.0  0.0  0.0
+    'ff5fd9c3c624b7dc_jpg.rf.f0751c9c3aa4519ea3c9d6a...'  1.0  0.0  0.0  0.0  0.0  0.0
+    'fffe28b31f2a70d4_jpg.rf.7ea16bd637ba0711c53b540...'  0.0  6.0  0.0  0.0  0.0  0.0
+    ```
+
+The rows index the label files, each corresponding to an image in your dataset, and the columns correspond to your class-label indices. Each row represents a pseudo feature-vector, with the count of each class-label present in your dataset. This data structure enables the application of K-Fold Cross Validation to an object detection dataset.
+
+## K-Fold Dataset Split
+
+1. Now we will use the `KFold` class from `sklearn.model_selection` to generate `k` splits of the dataset.
+
+    - Important:
+        - Setting `shuffle=True` ensures a randomized distribution of classes in your splits.
+        - By setting `random_state=M` where `M` is a chosen integer, you can obtain repeatable results.
+
+    ```python
+    ksplit = 5
+    kf = KFold(n_splits=ksplit, shuffle=True, random_state=20)   # setting random_state for repeatable results
+
+    kfolds = list(kf.split(labels_df))
+    ```
+
+2. The dataset has now been split into `k` folds, each having a list of `train` and `val` indices. We will construct a DataFrame to display these results more clearly.
+
+    ```python
+    folds = [f'split_{n}' for n in range(1, ksplit + 1)]
+    folds_df = pd.DataFrame(index=indx, columns=folds)
+    
+    for idx, (train, val) in enumerate(kfolds, start=1):
+        folds_df[f'split_{idx}'].loc[labels_df.iloc[train].index] = 'train'
+        folds_df[f'split_{idx}'].loc[labels_df.iloc[val].index] = 'val'
+    ```
+
+3. Now we will calculate the distribution of class labels for each fold as a ratio of the classes present in `val` to those present in `train`.
+
+    ```python
+    fold_lbl_distrb = pd.DataFrame(index=folds, columns=cls_idx)
+    
+    for n, (train_indices, val_indices) in enumerate(kfolds, start=1):
+        train_totals = labels_df.iloc[train_indices].sum()
+        val_totals = labels_df.iloc[val_indices].sum()
+    
+        # To avoid division by zero, we add a small value (1E-7) to the denominator
+        ratio = val_totals / (train_totals + 1E-7)
+        fold_lbl_distrb.loc[f'split_{n}'] = ratio
+    ```
+
+The ideal scenario is for all class ratios to be reasonably similar for each split and across classes. This, however, will be subject to the specifics of your dataset.
+
+4. Next, we create the directories and dataset YAML files for each split.
+
+    ```python
+    save_path = Path(dataset_path / f'{datetime.date.today().isoformat()}_{ksplit}-Fold_Cross-val')
+    save_path.mkdir(parents=True, exist_ok=True)
+    
+    images = sorted((dataset_path / 'images').rglob("*.jpg"))  # change file extension as needed
+    ds_yamls = []
+    
+    for split in folds_df.columns:
+        # Create directories
+        split_dir = save_path / split
+        split_dir.mkdir(parents=True, exist_ok=True)
+        (split_dir / 'train' / 'images').mkdir(parents=True, exist_ok=True)
+        (split_dir / 'train' / 'labels').mkdir(parents=True, exist_ok=True)
+        (split_dir / 'val' / 'images').mkdir(parents=True, exist_ok=True)
+        (split_dir / 'val' / 'labels').mkdir(parents=True, exist_ok=True)
+    
+        # Create dataset YAML files
+        dataset_yaml = split_dir / f'{split}_dataset.yaml'
+        ds_yamls.append(dataset_yaml)
+    
+        with open(dataset_yaml, 'w') as ds_y:
+            yaml.safe_dump({
+                'path': split_dir.as_posix(),
+                'train': 'train',
+                'val': 'val',
+                'names': classes
+            }, ds_y)
+    ```
+
+5. Lastly, copy images and labels into the respective directory ('train' or 'val') for each split.
+
+    - __NOTE:__ The time required for this portion of the code will vary based on the size of your dataset and your system hardware.
+
+    ```python
+    for image, label in zip(images, labels):
+        for split, k_split in folds_df.loc[image.stem].items():
+            # Destination directory
+            img_to_path = save_path / split / k_split / 'images'
+            lbl_to_path = save_path / split / k_split / 'labels'
+        
+            # Copy image and label files to new directory 
+            # Might throw a SamefileError if file already exists
+            shutil.copy(image, img_to_path / image.name)
+            shutil.copy(label, lbl_to_path / label.name)
+    ```
+
+## Save Records (Optional)
+
+Optionally, you can save the records of the K-Fold split and label distribution DataFrames as CSV files for future reference.
+
+```python
+folds_df.to_csv(save_path / "kfold_datasplit.csv")
+fold_lbl_distrb.to_csv(save_path / "kfold_label_distribution.csv")
+```
+
+## Train YOLO using K-Fold Data Splits
+
+1. First, load the YOLO model.
+
+    ```python
+    weights_path = 'path/to/weights.pt'
+    model = YOLO(weights_path, task='detect')
+    ```
+
+2. Next, iterate over the dataset YAML files to run training. The results will be saved to a directory specified by the `project` and `name` arguments. By default, this directory is 'exp/runs#' where # is an integer index.
+
+    ```python
+    results = {}
+    for k in range(ksplit):
+        dataset_yaml = ds_yamls[k]
+        model.train(data=dataset_yaml, *args, **kwargs)  # Include any training arguments
+        results[k] = model.metrics  # save output metrics for further analysis
+    ```
+
+## Conclusion
+
+In this guide, we have explored the process of using K-Fold cross-validation for training the YOLO object detection model. We learned how to split our dataset into K partitions, ensuring a balanced class distribution across the different folds.
+
+We also explored the procedure for creating report DataFrames to visualize the data splits and label distributions across these splits, providing us a clear insight into the structure of our training and validation sets.
+
+Optionally, we saved our records for future reference, which could be particularly useful in large-scale projects or when troubleshooting model performance.
+
+Finally, we implemented the actual model training using each split in a loop, saving our training results for further analysis and comparison.
+
+This technique of K-Fold cross-validation is a robust way of making the most out of your available data, and it helps to ensure that your model performance is reliable and consistent across different data subsets. This results in a more generalizable and reliable model that is less likely to overfit to specific data patterns.
+
+Remember that although we used YOLO in this guide, these steps are mostly transferable to other machine learning models. Understanding these steps allows you to apply cross-validation effectively in your own machine learning projects. Happy coding!

downloads/ultralytics-main/docs/help/CI.md

@@ -0,0 +1,62 @@
+---
+comments: true
+description: Learn how Ultralytics leverages Continuous Integration (CI) for maintaining high-quality code. Explore our CI tests and the status of these tests for our repositories.
+keywords: continuous integration, software development, CI tests, Ultralytics repositories, high-quality code, Docker Deployment, Broken Links, CodeQL, PyPi Publishing
+---
+
+# Continuous Integration (CI)
+
+Continuous Integration (CI) is an essential aspect of software development which involves integrating changes and testing them automatically. CI allows us to maintain high-quality code by catching issues early and often in the development process. At Ultralytics, we use various CI tests to ensure the quality and integrity of our codebase.
+
+## CI Actions
+
+Here's a brief description of our CI actions:
+
+- **CI:** This is our primary CI test that involves running unit tests, linting checks, and sometimes more comprehensive tests depending on the repository.
+- **Docker Deployment:** This test checks the deployment of the project using Docker to ensure the Dockerfile and related scripts are working correctly.
+- **Broken Links:** This test scans the codebase for any broken or dead links in our markdown or HTML files.
+- **CodeQL:** CodeQL is a tool from GitHub that performs semantic analysis on our code, helping to find potential security vulnerabilities and maintain high-quality code.
+- **PyPi Publishing:** This test checks if the project can be packaged and published to PyPi without any errors.
+
+### CI Results
+
+Below is the table showing the status of these CI tests for our main repositories:
+
+| Repository                                                | CI                                                                                                                                                                        | Docker Deployment                                                                                                                                                                        | Broken Links                                                                                                                                                                      | CodeQL                                                                                                                                                                          | PyPi and Docs Publishing                                                                                                                                                                                      |
+|-----------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [yolov3](https://github.com/ultralytics/yolov3)           | [![YOLOv3 CI](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg)](https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml)  | [![Publish Docker Images](https://github.com/ultralytics/yolov3/actions/workflows/docker.yml/badge.svg)](https://github.com/ultralytics/yolov3/actions/workflows/docker.yml)             | [![Check Broken links](https://github.com/ultralytics/yolov3/actions/workflows/links.yml/badge.svg)](https://github.com/ultralytics/yolov3/actions/workflows/links.yml)           | [![CodeQL](https://github.com/ultralytics/yolov3/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/ultralytics/yolov3/actions/workflows/codeql-analysis.yml) |                                                                                                                                                                                                               |
+| [yolov5](https://github.com/ultralytics/yolov5)           | [![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml)  | [![Publish Docker Images](https://github.com/ultralytics/yolov5/actions/workflows/docker.yml/badge.svg)](https://github.com/ultralytics/yolov5/actions/workflows/docker.yml)             | [![Check Broken links](https://github.com/ultralytics/yolov5/actions/workflows/links.yml/badge.svg)](https://github.com/ultralytics/yolov5/actions/workflows/links.yml)           | [![CodeQL](https://github.com/ultralytics/yolov5/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/ultralytics/yolov5/actions/workflows/codeql-analysis.yml) |                                                                                                                                                                                                               |
+| [ultralytics](https://github.com/ultralytics/ultralytics) | [![ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml) | [![Publish Docker Images](https://github.com/ultralytics/ultralytics/actions/workflows/docker.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/docker.yaml) | [![Check Broken links](https://github.com/ultralytics/ultralytics/actions/workflows/links.yml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/links.yml) | [![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml)       | [![Publish to PyPI and Deploy Docs](https://github.com/ultralytics/ultralytics/actions/workflows/publish.yml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/publish.yml)            |
+| [hub](https://github.com/ultralytics/hub)                 | [![HUB CI](https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg)](https://github.com/ultralytics/hub/actions/workflows/ci.yaml)                         |                                                                                                                                                                                          | [![Check Broken links](https://github.com/ultralytics/hub/actions/workflows/links.yml/badge.svg)](https://github.com/ultralytics/hub/actions/workflows/links.yml)                 |                                                                                                                                                                                 |                                                                                                                                                                                                               |
+| [docs](https://github.com/ultralytics/docs)               |                                                                                                                                                                           |                                                                                                                                                                                          |                                                                                                                                                                                   |                                                                                                                                                                                 | [![pages-build-deployment](https://github.com/ultralytics/docs/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/pages/pages-build-deployment) |
+
+Each badge shows the status of the last run of the corresponding CI test on the `main` branch of the respective repository. If a test fails, the badge will display a "failing" status, and if it passes, it will display a "passing" status.
+
+If you notice a test failing, it would be a great help if you could report it through a GitHub issue in the respective repository.
+
+Remember, a successful CI test does not mean that everything is perfect. It is always recommended to manually review the code before deployment or merging changes.
+
+## Code Coverage
+
+Code coverage is a metric that represents the percentage of your codebase that is executed when your tests run. It provides insight into how well your tests exercise your code and can be crucial in identifying untested parts of your application. A high code coverage percentage is often associated with a lower likelihood of bugs. However, it's essential to understand that code coverage doesn't guarantee the absence of defects. It merely indicates which parts of the code have been executed by the tests.
+
+### Integration with [codecov.io](https://codecov.io/)
+
+At Ultralytics, we have integrated our repositories with [codecov.io](https://codecov.io/), a popular online platform for measuring and visualizing code coverage. Codecov provides detailed insights, coverage comparisons between commits, and visual overlays directly on your code, indicating which lines were covered.
+
+By integrating with Codecov, we aim to maintain and improve the quality of our code by focusing on areas that might be prone to errors or need further testing.
+
+### Coverage Results
+
+To quickly get a glimpse of the code coverage status of the `ultralytics` python package, we have included a badge and and sunburst visual of the `ultralytics` coverage results. These images show the percentage of code covered by our tests, offering an at-a-glance metric of our testing efforts. For full details please see https://codecov.io/github/ultralytics/ultralytics.
+
+| Repository                                                | Code Coverage                                                        |
+|-----------------------------------------------------------|----------------------------------------------------------------------|
+| [ultralytics](https://github.com/ultralytics/ultralytics) | [![codecov](https://codecov.io/gh/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY)](https://codecov.io/gh/ultralytics/ultralytics) |
+
+In the sunburst graphic below, the inner-most circle is the entire project, moving away from the center are folders then, finally, a single file. The size and color of each slice is representing the number of statements and the coverage, respectively.
+
+<a href="https://codecov.io/github/ultralytics/ultralytics">
+    <img src="https://codecov.io/gh/ultralytics/ultralytics/branch/main/graphs/sunburst.svg?token=HHW7IIVFVY" alt="Ultralytics Codecov Image">
+</a>
+

downloads/ultralytics-main/docs/help/CLA.md

@@ -1,5 +1,6 @@
 ---
-description: Individual Contributor License Agreement. Settle Intellectual Property issues for Contributions made to anything open source released by Ultralytics.
+description: Understand terms governing contributions to Ultralytics projects including source code, bug fixes, documentation and more. Read our Contributor License Agreement.
+keywords: Ultralytics, Contributor License Agreement, Open Source Software, Contributions, Copyright License, Patent License, Moral Rights
 ---
 
 # Ultralytics Individual Contributor License Agreement
@@ -66,4 +67,4 @@ that any of the provisions of this Agreement shall be held by a court or other t
 to be unenforceable, the remaining portions hereof shall remain in full force and effect.
 
 **Assignment.** You agree that Ultralytics may assign this Agreement, and all of its rights, obligations and licenses
-hereunder.
+hereunder.

downloads/ultralytics-main/docs/help/FAQ.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: 'Get quick answers to common Ultralytics YOLO questions: Hardware requirements, fine-tuning, conversion, real-time detection, and accuracy tips.'
+description: Find solutions to your common Ultralytics YOLO related queries. Learn about hardware requirements, fine-tuning YOLO models, conversion to ONNX/TensorFlow, and more.
+keywords: Ultralytics, YOLO, FAQ, hardware requirements, ONNX, TensorFlow, real-time detection, YOLO accuracy
 ---
 
 # Ultralytics YOLO Frequently Asked Questions (FAQ)
@@ -35,4 +36,4 @@ Improving the accuracy of a YOLO model may involve several strategies, such as:
 
 Remember that there's often a trade-off between accuracy and inference speed, so finding the right balance is crucial for your specific application.
 
-If you have any more questions or need assistance, don't hesitate to consult the Ultralytics documentation or reach out to the community through GitHub Issues or the official discussion forum.
+If you have any more questions or need assistance, don't hesitate to consult the Ultralytics documentation or reach out to the community through GitHub Issues or the official discussion forum.

downloads/ultralytics-main/docs/help/code_of_conduct.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Read the Ultralytics Contributor Covenant Code of Conduct. Learn ways to create a welcoming community & consequences for inappropriate conduct.
+description: Explore Ultralytics community’s Code of Conduct, ensuring a supportive, inclusive environment for contributors & members at all levels. Find our guidelines on acceptable behavior & enforcement.
+keywords: Ultralytics, code of conduct, community, contribution, behavior guidelines, enforcement, open source contributions
 ---
 
 # Ultralytics Contributor Covenant Code of Conduct
@@ -130,4 +131,4 @@ For answers to common questions about this code of conduct, see the FAQ at
 https://www.contributor-covenant.org/faq. Translations are available at
 https://www.contributor-covenant.org/translations.
 
-[homepage]: https://www.contributor-covenant.org
+[homepage]: https://www.contributor-covenant.org

downloads/ultralytics-main/docs/help/contributing.md

@@ -1,11 +1,12 @@
 ---
 comments: true
-description: Learn how to contribute to Ultralytics Open-Source YOLO Repositories with contributions guidelines, pull requests requirements, and GitHub CI tests.
+description: Learn how to contribute to Ultralytics YOLO projects – guidelines for pull requests, reporting bugs, code conduct and CLA signing.
+keywords: Ultralytics, YOLO, open-source, contribute, pull request, bug report, coding guidelines, CLA, code of conduct, GitHub
 ---
 
 # Contributing to Ultralytics Open-Source YOLO Repositories
 
-First of all, thank you for your interest in contributing to Ultralytics open-source YOLO repositories! Your contributions will help improve the project and benefit the community. This document provides guidelines and best practices for contributing to Ultralytics YOLO repositories.
+First of all, thank you for your interest in contributing to Ultralytics open-source YOLO repositories! Your contributions will help improve the project and benefit the community. This document provides guidelines and best practices to get you started.
 
 ## Table of Contents
 
@@ -71,4 +72,4 @@ def example_function(arg1: int, arg2: str) -> bool:
 
 ### GitHub Actions CI Tests
 
-Before your pull request can be merged, all GitHub Actions Continuous Integration (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure that your changes meet the quality standards of the project. Make sure to review the output of the GitHub Actions and fix any issues
+Before your pull request can be merged, all GitHub Actions Continuous Integration (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure that your changes meet the quality standards of the project. Make sure to review the output of the GitHub Actions and fix any issues

downloads/ultralytics-main/docs/help/environmental-health-safety.md

@@ -0,0 +1,37 @@
+---
+comments: false
+description: Discover Ultralytics’ EHS policy principles and implementation measures. Committed to safety, environment, and continuous improvement for a sustainable future.
+keywords: Ultralytics policy, EHS, environment, health and safety, compliance, prevention, continuous improvement, risk management, emergency preparedness, resource allocation, communication
+---
+
+# Ultralytics Environmental, Health and Safety (EHS) Policy
+
+At Ultralytics, we recognize that the long-term success of our company relies not only on the products and services we offer, but also the manner in which we conduct our business. We are committed to ensuring the safety and well-being of our employees, stakeholders, and the environment, and we will continuously strive to mitigate our impact on the environment while promoting health and safety.
+
+## Policy Principles
+
+1. **Compliance**: We will comply with all applicable laws, regulations, and standards related to EHS, and we will strive to exceed these standards where possible.
+
+2. **Prevention**: We will work to prevent accidents, injuries, and environmental harm by implementing risk management measures and ensuring all our operations and procedures are safe.
+
+3. **Continuous Improvement**: We will continuously improve our EHS performance by setting measurable objectives, monitoring our performance, auditing our operations, and revising our policies and procedures as needed.
+
+4. **Communication**: We will communicate openly about our EHS performance and will engage with stakeholders to understand and address their concerns and expectations.
+
+5. **Education and Training**: We will educate and train our employees and contractors in appropriate EHS procedures and practices.
+
+## Implementation Measures
+
+1. **Responsibility and Accountability**: Every employee and contractor working at or with Ultralytics is responsible for adhering to this policy. Managers and supervisors are accountable for ensuring this policy is implemented within their areas of control.
+
+2. **Risk Management**: We will identify, assess, and manage EHS risks associated with our operations and activities to prevent accidents, injuries, and environmental harm.
+
+3. **Resource Allocation**: We will allocate the necessary resources to ensure the effective implementation of our EHS policy, including the necessary equipment, personnel, and training.
+
+4. **Emergency Preparedness and Response**: We will develop, maintain, and test emergency preparedness and response plans to ensure we can respond effectively to EHS incidents.
+
+5. **Monitoring and Review**: We will monitor and review our EHS performance regularly to identify opportunities for improvement and ensure we are meeting our objectives.
+
+This policy reflects our commitment to minimizing our environmental footprint, ensuring the safety and well-being of our employees, and continuously improving our performance.
+
+Please remember that the implementation of an effective EHS policy requires the involvement and commitment of everyone working at or with Ultralytics. We encourage you to take personal responsibility for your safety and the safety of others, and to take care of the environment in which we live and work.

downloads/ultralytics-main/docs/help/index.md

@@ -1,15 +1,18 @@
 ---
 comments: true
-description: Get comprehensive resources for Ultralytics YOLO repositories. Find guides, FAQs, MRE creation, CLA & more. Join the supportive community now!
+description: Find comprehensive guides and documents on Ultralytics YOLO tasks. Includes FAQs, contributing guides, CI guide, CLA, MRE guide, code of conduct & more.
+keywords: Ultralytics, YOLO, guides, documents, FAQ, contributing, CI guide, CLA, MRE guide, code of conduct, EHS policy, security policy
 ---
 
 Welcome to the Ultralytics Help page! We are committed to providing you with comprehensive resources to make your experience with Ultralytics YOLO repositories as smooth and enjoyable as possible. On this page, you'll find essential links to guides and documents that will help you navigate through common tasks and address any questions you might have while using our repositories.
 
 - [Frequently Asked Questions (FAQ)](FAQ.md): Find answers to common questions and issues faced by users and contributors of Ultralytics YOLO repositories.
 - [Contributing Guide](contributing.md): Learn the best practices for submitting pull requests, reporting bugs, and contributing to the development of our repositories.
+- [Continuous Integration (CI) Guide](CI.md): Understand the CI tests we perform for each Ultralytics repository and see their current statuses.
 - [Contributor License Agreement (CLA)](CLA.md): Familiarize yourself with our CLA to understand the terms and conditions for contributing to Ultralytics projects.
 - [Minimum Reproducible Example (MRE) Guide](minimum_reproducible_example.md): Understand how to create an MRE when submitting bug reports to ensure that our team can quickly and efficiently address the issue.
 - [Code of Conduct](code_of_conduct.md): Learn about our community guidelines and expectations to ensure a welcoming and inclusive environment for all participants.
+- [Environmental, Health and Safety (EHS) Policy](environmental-health-safety.md): Explore Ultralytics' dedicated approach towards maintaining a sustainable, safe, and healthy work environment for all our stakeholders.
 - [Security Policy](../SECURITY.md): Understand our security practices and how to report security vulnerabilities responsibly.
 
-We highly recommend going through these guides to make the most of your collaboration with the Ultralytics community. Our goal is to maintain a welcoming and supportive environment for all users and contributors. If you need further assistance, don't hesitate to reach out to us through GitHub Issues or the official discussion forum. Happy coding!
+We highly recommend going through these guides to make the most of your collaboration with the Ultralytics community. Our goal is to maintain a welcoming and supportive environment for all users and contributors. If you need further assistance, don't hesitate to reach out to us through GitHub Issues or the official discussion forum. Happy coding!

downloads/ultralytics-main/docs/help/minimum_reproducible_example.md

@@ -1,11 +1,12 @@
 ---
 comments: true
-description: Learn how to create a Minimum Reproducible Example (MRE) for Ultralytics YOLO bug reports to help maintainers and contributors understand your issue better.
+description: Learn how to create minimum reproducible examples (MRE) for efficient bug reporting in Ultralytics YOLO repositories with this step-by-step guide.
+keywords: Ultralytics, YOLO, minimum reproducible example, MRE, bug reports, guide, dependencies, code, troubleshooting
 ---
 
 # Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories
 
-When submitting a bug report for Ultralytics YOLO repositories, it's essential to provide a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) (MRE). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories.
+When submitting a bug report for Ultralytics YOLO repositories, it's essential to provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) (MRE). An MRE is a small, self-contained piece of code that demonstrates the problem you're experiencing. Providing an MRE helps maintainers and contributors understand the issue and work on a fix more efficiently. This guide explains how to create an MRE when submitting bug reports to Ultralytics YOLO repositories.
 
 ## 1. Isolate the Problem
 
@@ -74,4 +75,4 @@ RuntimeError: Expected input[1, 0, 640, 640] to have 3 channels, but got 0 chann
 
 In this example, the MRE demonstrates the issue with a minimal amount of code, uses a public model ('yolov8n.pt'), includes all necessary dependencies, and provides a clear description of the problem along with the error message.
 
-By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.
+By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.

downloads/ultralytics-main/docs/hub/app/android.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Run YOLO models on your Android device for real-time object detection with Ultralytics Android App. Utilizes TensorFlow Lite and hardware delegates.
+description: Learn about the Ultralytics Android App, enabling real-time object detection using YOLO models. Discover in-app features, quantization methods, and delegate options for optimal performance.
+keywords: Ultralytics, Android App, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, CPU, GPU, Hexagon, NNAPI
 ---
 
 # Ultralytics Android App: Real-time Object Detection with YOLO Models
@@ -62,4 +63,4 @@ To get started with the Ultralytics Android App, follow these steps:
 
 6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
 
-With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
+With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.

downloads/ultralytics-main/docs/hub/app/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Experience the power of YOLOv5 and YOLOv8 models with Ultralytics HUB app. Download from Google Play and App Store now.
+description: Explore the Ultralytics HUB App, offering the ability to run YOLOv5 and YOLOv8 models on your iOS and Android devices with optimized performance.
+keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object detection, image recognition, mobile device, hardware acceleration, Apple Neural Engine, Android GPU, NNAPI, custom model training
 ---
 
 # Ultralytics HUB App
@@ -12,7 +13,7 @@ description: Experience the power of YOLOv5 and YOLOv8 models with Ultralytics H
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@@ -48,4 +49,4 @@ Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mo
 - [**iOS**](./ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
 - [**Android**](./android.md): Explore TFLite acceleration on Android mobile devices.
 
-Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
+Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.

downloads/ultralytics-main/docs/hub/app/ios.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Get started with the Ultralytics iOS app and run YOLO models in real-time for object detection on your iPhone or iPad with the Apple Neural Engine.
+description: Execute object detection in real-time on your iOS devices utilizing YOLO models. Leverage the power of the Apple Neural Engine and Core ML for fast and efficient object detection.
+keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple Neural Engine, Core ML, FP16, INT8, quantization
 ---
 
 # Ultralytics iOS App: Real-time Object Detection with YOLO Models
@@ -52,4 +53,4 @@ To get started with the Ultralytics iOS App, follow these steps:
 
 6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
 
-With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
+With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.

downloads/ultralytics-main/docs/hub/datasets.md

@@ -1,50 +1,159 @@
 ---
 comments: true
-description: Upload custom datasets to Ultralytics HUB for YOLOv5 and YOLOv8 models. Follow YAML structure, zip and upload. Scan & train new models.
+description: Learn how Ultralytics HUB datasets streamline your ML workflow. Upload, format, validate, access, share, edit or delete datasets for Ultralytics YOLO model training.
+keywords: Ultralytics, HUB datasets, YOLO model training, upload datasets, dataset validation, ML workflow, share datasets
 ---
 
 # HUB Datasets
 
-## 1. Upload a Dataset
+Ultralytics HUB datasets are a practical solution for managing and leveraging your custom datasets.
 
-Ultralytics HUB datasets are just like YOLOv5 and YOLOv8 🚀 datasets, they use the same structure and the same label formats to keep
+Once uploaded, datasets can be immediately utilized for model training. This integrated approach facilitates a seamless transition from dataset management to model training, significantly simplifying the entire process.
+
+## Upload Dataset
+
+Ultralytics HUB datasets are just like YOLOv5 and YOLOv8 🚀 datasets. They use the same structure and the same label formats to keep
 everything simple.
 
-When you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML inside the dataset root directory**
-as in the example shown below, and then zip for upload to [https://hub.ultralytics.com](https://hub.ultralytics.com/). Your **dataset YAML, directory
-and zip** should all share the same name. For example, if your dataset is called 'coco8' as in our
-example [ultralytics/hub/example_datasets/coco8.zip](https://github.com/ultralytics/hub/blob/master/example_datasets/coco8.zip), then you should have a `coco8.yaml` inside your `coco8/` directory, which should zip to create `coco8.zip` for upload:
+Before you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML file inside the dataset root directory** and that **your dataset YAML, directory and ZIP have the same name**, as shown in the example below, and then zip the dataset directory.
+
+For example, if your dataset is called "coco8", as our [COCO8](https://docs.ultralytics.com/datasets/detect/coco8) example dataset, then you should have a `coco8.yaml` inside your `coco8/` directory, which will create a `coco8.zip` when zipped:
 
 ```bash
 zip -r coco8.zip coco8
 ```
 
-The [example_datasets/coco8.zip](https://github.com/ultralytics/hub/blob/master/example_datasets/coco8.zip) dataset in this repository can be downloaded and unzipped to see exactly how to structure your custom dataset.
+You can download our [COCO8](https://github.com/ultralytics/hub/blob/master/example_datasets/coco8.zip) example dataset and unzip it to see exactly how to structure your dataset.
 
 <p align="center">
-<img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO8" />
+  <img  src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_1.jpg" alt="COCO8 Dataset Structure" width="80%" />
 </p>
 
-The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format. See
-the [YOLOv5 and YOLOv8 Train Custom Data tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) for full details.
+The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format.
 
-```yaml
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path:  # dataset root dir (leave empty for HUB)
-train: images/train  # train images (relative to 'path') 8 images
-val: images/val  # val images (relative to 'path') 8 images
-test:  # test images (optional)
+!!! example "coco8.yaml"
 
-# Classes
-names:
-  0: person
-  1: bicycle
-  2: car
-  3: motorcycle
-  ...
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco8.yaml"
+    ```
+
+After zipping your dataset, you should validate it before uploading it to Ultralytics HUB. Ultralytics HUB conducts the dataset validation check post-upload, so by ensuring your dataset is correctly formatted and error-free ahead of time, you can forestall any setbacks due to dataset rejection.
+
+```py
+from ultralytics.hub import check_dataset
+check_dataset('path/to/coco8.zip')
 ```
 
-After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab.
-Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 or YOLOv8 models on it!
+Once your dataset ZIP is ready, navigate to the [Datasets](https://hub.ultralytics.com/datasets) page by clicking on the **Datasets** button in the sidebar.
 
-<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/216763338-9a8812c8-a4e5-4362-8102-40dad7818396.png">
+![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Datasets button in the sidebar](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_2.jpg)
+
+??? tip "Tip"
+
+    You can also upload a dataset directly from the [Home](https://hub.ultralytics.com/home) page.
+
+    ![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Upload Dataset card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_3.jpg)
+
+Click on the **Upload Dataset** button on the top right of the page. This action will trigger the **Upload Dataset** dialog.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Upload Dataset button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_4.jpg)
+
+Upload your dataset in the _Dataset .zip file_ field.
+
+You have the additional option to set a custom name and description for your Ultralytics HUB dataset.
+
+When you're happy with your dataset configuration, click **Upload**.
+
+![Ultralytics HUB screenshot of the Upload Dataset dialog with an arrow pointing to the Upload button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_5.jpg)
+
+After your dataset is uploaded and processed, you will be able to access it from the Datasets page.
+
+![Ultralytics HUB screenshot of the Datasets page with an arrow pointing to one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_6.jpg)
+
+You can view the images in your dataset grouped by splits (Train, Validation, Test).
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Images tab](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_7.jpg)
+
+??? tip "Tip"
+
+    Each image can be enlarged for better visualization.
+
+    ![Ultralytics HUB screenshot of the Images tab inside the Dataset page with an arrow pointing to the expand icon](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_8.jpg)
+
+    ![Ultralytics HUB screenshot of the Images tab inside the Dataset page with one of the images expanded](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_9.jpg)
+
+Also, you can analyze your dataset by click on the **Overview** tab.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Overview tab](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_10.jpg)
+
+Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) on your dataset.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Train Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_11.jpg)
+
+## Share Dataset
+
+!!! info "Info"
+
+    Ultralytics HUB's sharing functionality provides a convenient way to share datasets with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
+
+??? note "Note"
+
+    You have control over the general access of your datasets.
+
+    You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the dataset, regardless of whether they have an Ultralytics HUB account or not.
+
+Navigate to the Dataset page of the dataset you want to share, open the dataset actions dropdown and click on the **Share** option. This action will trigger the **Share Dataset** dialog.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Share option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_share_dataset_1.jpg)
+
+??? tip "Tip"
+
+    You can also share a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
+
+    ![Ultralytics HUB screenshot of the Datasets page with an arrow pointing to the Share option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_share_dataset_2.jpg)
+
+Set the general access to "Unlisted" and click **Save**.
+
+![Ultralytics HUB screenshot of the Share Dataset dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_share_dataset_3.jpg)
+
+Now, anyone who has the direct link to your dataset can view it.
+
+??? tip "Tip"
+
+    You can easily click on the dataset's link shown in the **Share Dataset** dialog to copy it.
+
+    ![Ultralytics HUB screenshot of the Share Dataset dialog with an arrow pointing to the dataset's link](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_share_dataset_4.jpg)
+
+## Edit Dataset
+
+Navigate to the Dataset page of the dataset you want to edit, open the dataset actions dropdown and click on the **Edit** option. This action will trigger the **Update Dataset** dialog.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Edit option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_edit_dataset_1.jpg)
+
+??? tip "Tip"
+
+    You can also edit a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
+
+    ![Ultralytics HUB screenshot of the Datasets page with an arrow pointing to the Edit option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_edit_dataset_2.jpg)
+
+Apply the desired modifications to your dataset and then confirm the changes by clicking **Save**.
+
+![Ultralytics HUB screenshot of the Update Dataset dialog with an arrow pointing to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_edit_dataset_3.jpg)
+
+## Delete Dataset
+
+Navigate to the Dataset page of the dataset you want to delete, open the dataset actions dropdown and click on the **Delete** option. This action will delete the dataset.
+
+![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Delete option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_1.jpg)
+
+??? tip "Tip"
+
+    You can also delete a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
+
+    ![Ultralytics HUB screenshot of the Datasets page with an arrow pointing to the Delete option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_2.jpg)
+
+??? note "Note"
+
+    If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
+
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_3.jpg)

downloads/ultralytics-main/docs/hub/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: 'Ultralytics HUB: Train & deploy YOLO models from one spot! Use drag-and-drop interface with templates & pre-training models. Check quickstart, datasets, and more.'
+description: Gain seamless experience in training and deploying your YOLOv5 and YOLOv8 models with Ultralytics HUB. Explore pre-trained models, templates and various integrations.
+keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pretrained models, model integrations
 ---
 
 # Ultralytics HUB
@@ -28,7 +29,7 @@ easily upload their data and train new models quickly. It offers a range of pre-
 templates to choose from, making it easy for users to get started with training their own models. Once a model is
 trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
 
-We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.gg/n6cFeSPZdD">Discord</a> community for questions and discussions!
+We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
 
 - [**Quickstart**](./quickstart.md). Start training and deploying YOLO models with HUB in seconds.
 - [**Datasets: Preparing and Uploading**](./datasets.md). Learn how to prepare and upload your datasets to HUB in YOLO format.
@@ -38,4 +39,4 @@ We hope that the resources here will help you get the most out of HUB. Please br
 - [**Ultralytics HUB App**](./app/index.md). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
     * [**iOS**](./app/ios.md). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
     * [**Android**](./app/android.md). Explore TFLite acceleration on mobile devices.
-- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
+- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.

downloads/ultralytics-main/docs/hub/inference_api.md

@@ -1,15 +1,16 @@
 ---
 comments: true
+description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or CLI for swift object detection.
+keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, CLI, Quickstart
 ---
 
-# 🚧 Page Under Construction ⚒
-
-This page is currently under construction!️ 👷Please check back later for updates. 😃🔜
-
 # YOLO Inference API
 
 The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
 
+![Inference API Screenshot](https://github.com/ultralytics/ultralytics/assets/26833433/c0109ec0-7bb0-46e1-b0d2-bae687960a01)
+Screenshot of the Inference API section in the trained model Preview tab.
+
 ## API URL
 
 The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:
@@ -110,7 +111,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
     === "Local"
         ```python
         from ultralytics import YOLO
-        
+
         # Load model
         model = YOLO('yolov8n.pt')
 
@@ -118,12 +119,12 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
         results = model('image.jpg')
 
         # Print image.jpg results in JSON format
-        print(results[0].tojson())  
+        print(results[0].tojson())
         ```
 
     === "CLI API"
         ```bash
-        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \ 
+        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
             -H "x-api-key: API_KEY" \
             -F "image=@/path/to/image.jpg" \
             -F "size=640" \
@@ -134,21 +135,21 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
     === "Python API"
         ```python
         import requests
-        
+
         # API URL, use actual MODEL_ID
         url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
-        
+
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}
-        
+
         # Inference arguments (optional)
         data = {"size": 640, "confidence": 0.25, "iou": 0.45}
-        
+
         # Load image and send request
         with open("path/to/image.jpg", "rb") as image_file:
             files = {"image": image_file}
             response = requests.post(url, headers=headers, files=files, data=data)
-        
+
         print(response.json())
         ```
 
@@ -204,7 +205,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
     === "Local"
         ```python
         from ultralytics import YOLO
-        
+
         # Load model
         model = YOLO('yolov8n-seg.pt')
 
@@ -212,12 +213,12 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
         results = model('image.jpg')
 
         # Print image.jpg results in JSON format
-        print(results[0].tojson())  
+        print(results[0].tojson())
         ```
 
     === "CLI API"
         ```bash
-        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \ 
+        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
             -H "x-api-key: API_KEY" \
             -F "image=@/path/to/image.jpg" \
             -F "size=640" \
@@ -228,21 +229,21 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
     === "Python API"
         ```python
         import requests
-        
+
         # API URL, use actual MODEL_ID
         url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
-        
+
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}
-        
+
         # Inference arguments (optional)
         data = {"size": 640, "confidence": 0.25, "iou": 0.45}
-        
+
         # Load image and send request
         with open("path/to/image.jpg", "rb") as image_file:
             files = {"image": image_file}
             response = requests.post(url, headers=headers, files=files, data=data)
-        
+
         print(response.json())
         ```
 
@@ -341,7 +342,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
     === "Local"
         ```python
         from ultralytics import YOLO
-        
+
         # Load model
         model = YOLO('yolov8n-seg.pt')
 
@@ -349,12 +350,12 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
         results = model('image.jpg')
 
         # Print image.jpg results in JSON format
-        print(results[0].tojson())  
+        print(results[0].tojson())
         ```
 
     === "CLI API"
         ```bash
-        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \ 
+        curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
             -H "x-api-key: API_KEY" \
             -F "image=@/path/to/image.jpg" \
             -F "size=640" \
@@ -365,21 +366,21 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
     === "Python API"
         ```python
         import requests
-        
+
         # API URL, use actual MODEL_ID
         url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
-        
+
         # Headers, use actual API_KEY
         headers = {"x-api-key": "API_KEY"}
-        
+
         # Inference arguments (optional)
         data = {"size": 640, "confidence": 0.25, "iou": 0.45}
-        
+
         # Load image and send request
         with open("path/to/image.jpg", "rb") as image_file:
             files = {"image": image_file}
             response = requests.post(url, headers=headers, files=files, data=data)
-        
+
         print(response.json())
         ```
 
@@ -454,4 +455,4 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
             }
           ]
         }
-        ```
+        ```

downloads/ultralytics-main/docs/hub/models.md

@@ -1,20 +1,213 @@
 ---
 comments: true
-description: Train and Deploy your Model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle or directly on Mobile.
+description: Learn how to use Ultralytics HUB models for efficient and user-friendly AI model training. For easy model creation, training, evaluation and deployment, follow our detailed guide.
+keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
 ---
 
-# HUB Models
+# Ultralytics HUB Models
 
-## Train a Model
+Ultralytics HUB models provide a streamlined solution for training vision AI models on your custom datasets.
 
-Connect to the Ultralytics HUB notebook and use your model API key to begin training!
+The process is user-friendly and efficient, involving a simple three-step creation and accelerated training powered by Utralytics YOLOv8. During training, real-time updates on model metrics are available so that you can monitor each step of the progress. Once training is completed, you can preview your model and easily deploy it to real-world applications. Therefore, Ultralytics HUB offers a comprehensive yet straightforward system for model creation, training, evaluation, and deployment.
+
+## Train Model
+
+Navigate to the [Models](https://hub.ultralytics.com/models) page by clicking on the **Models** button in the sidebar.
+
+![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Models button in the sidebar](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_1.jpg)
+
+??? tip "Tip"
+
+    You can also train a model directly from the [Home](https://hub.ultralytics.com/home) page.
+
+    ![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Train Model card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_2.jpg)
+
+Click on the **Train Model** button on the top right of the page. This action will trigger the **Train Model** dialog.
+
+![Ultralytics HUB screenshot of the Models page with an arrow pointing to the Train Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_3.jpg)
+
+The **Train Model** dialog has three simple steps, explained below.
+
+### 1. Dataset
+
+In this step, you have to select the dataset you want to train your model on. After you selected a dataset, click **Continue**.
+
+![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to a dataset and one to the Continue button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_4.jpg)
+
+??? tip "Tip"
+
+    You can skip this step if you train a model directly from the Dataset page.
+
+    ![Ultralytics HUB screenshot of the Dataset page with an arrow pointing to the Train Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_5.jpg)
+
+### 2. Model
+
+In this step, you have to choose the project in which you want to create your model, the name of your model and your model's architecture.
+
+??? note "Note"
+
+    Ultralytics HUB will try to pre-select the project.
+
+    If you opened the **Train Model** dialog as described above, Ultralytics HUB will pre-select the last project you used.
+
+    If you opened the **Train Model** dialog from the Project page, Ultralytics HUB will pre-select the project you were inside of.
+
+    ![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Train Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_6.jpg)
+
+    In case you don't have a project created yet, you can set the name of your project in this step and it will be created together with your model.
+
+    ![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the project name](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_7.jpg)
+
+!!! info "Info"
+
+    You can read more about the available [YOLOv8](https://docs.ultralytics.com/models/yolov8) (and [YOLOv5](https://docs.ultralytics.com/models/yolov5)) architectures in our documentation.
+
+When you're happy with your model configuration, click **Continue**.
+
+![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to a model architecture and one to the Continue button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_8.jpg)
+
+??? note "Note"
+
+    By default, your model will use a pre-trained model (trained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset) to reduce training time.
+
+    You can change this behaviour by opening the **Advanced Options** accordion.
+
+### 3. Train
+
+In this step, you will start training you model.
+
+Ultralytics HUB offers three training options:
+
+- Ultralytics Cloud **(COMING SOON)**
+- Google Colab
+- Bring your own agent
+
+In order to start training your model, follow the instructions presented in this step.
+
+![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to each step](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_9.jpg)
+
+??? note "Note"
+
+    When you are on this step, before the training starts, you can change the default training configuration by opening the **Advanced Options** accordion.
+
+    ![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Train Advanced Options](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_10.jpg)
+
+??? note "Note"
+
+    When you are on this step, you have the option to close the **Train Model** dialog and start training your model from the Model page later.
+
+    ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Start Training card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_11.jpg)
+
+To start training your model using Google Colab, simply follow the instructions shown above or on the Google Colab notebook.
 
 <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
-<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
+</a>
 
-## Deploy to Real World
+When the training starts, you can click **Done** and monitor the training progress on the Model page.
 
-Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run
-models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or
-[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading
-the [Ultralytics App](https://ultralytics.com/app_install)!
+![Ultralytics HUB screenshot of the Train Model dialog with an arrow pointing to the Done button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_12.jpg)
+
+![Ultralytics HUB screenshot of the Model page of a model that is currently training](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_13.jpg)
+
+??? note "Note"
+
+    In case the training stops and a checkpoint was saved, you can resume training your model from the Model page.
+
+    ![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Resume Training card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_train_model_14.jpg)
+
+## Preview Model
+
+Ultralytics HUB offers a variety of ways to preview your trained model.
+
+You can preview your model if you click on the **Preview** tab and upload an image in the **Test** card.
+
+![Ultralytics HUB screenshot of the Preview tab (Test card) inside the Model page](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_preview_model_1.jpg)
+
+You can also use our Ultralytics Cloud API to effortlessly [run inference](https://docs.ultralytics.com/hub/inference_api) with your custom model.
+
+![Ultralytics HUB screenshot of the Preview tab (Ultralytics Cloud API card) inside the Model page](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_preview_model_2.jpg)
+
+Furthermore, you can preview your model in real-time directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by [downloading](https://ultralytics.com/app_install) our [Ultralytics HUB Mobile Application](./app/index.md).
+
+![Ultralytics HUB screenshot of the Deploy tab inside the Model page with arrow pointing to the Real-Time Preview card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_preview_model_3.jpg)
+
+## Deploy Model
+
+You can export your model to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle and many others.
+
+![Ultralytics HUB screenshot of the Deploy tab inside the Model page with all formats exported](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_deploy_model_1.jpg)
+
+??? tip "Tip"
+
+    You can customize the export options of each format if you open the export actions dropdown and click on the **Advanced** option.
+
+    ![Ultralytics HUB screenshot of the Deploy tab inside the Model page with an arrow pointing to the Advanced option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_deploy_model_2.jpg)
+
+## Share Model
+
+!!! info "Info"
+
+    Ultralytics HUB's sharing functionality provides a convenient way to share models with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
+
+??? note "Note"
+
+    You have control over the general access of your models.
+
+    You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the model, regardless of whether they have an Ultralytics HUB account or not.
+
+Navigate to the Model page of the model you want to share, open the model actions dropdown and click on the **Share** option. This action will trigger the **Share Model** dialog.
+
+![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Share option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_share_model_1.jpg)
+
+??? tip "Tip"
+
+    You can also share a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
+
+    ![Ultralytics HUB screenshot of the Models page with an arrow pointing to the Share option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_share_model_2.jpg)
+
+Set the general access to "Unlisted" and click **Save**.
+
+![Ultralytics HUB screenshot of the Share Model dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_share_model_3.jpg)
+
+Now, anyone who has the direct link to your model can view it.
+
+??? tip "Tip"
+
+    You can easily click on the models's link shown in the **Share Model** dialog to copy it.
+
+    ![Ultralytics HUB screenshot of the Share Model dialog with an arrow pointing to the model's link](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_share_model_4.jpg)
+
+## Edit Model
+
+Navigate to the Model page of the model you want to edit, open the model actions dropdown and click on the **Edit** option. This action will trigger the **Update Model** dialog.
+
+![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Edit option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_edit_model_1.jpg)
+
+??? tip "Tip"
+
+    You can also edit a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
+
+    ![Ultralytics HUB screenshot of the Models page with an arrow pointing to the Edit option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_edit_model_2.jpg)
+
+Apply the desired modifications to your model and then confirm the changes by clicking **Save**.
+
+![Ultralytics HUB screenshot of the Update Model dialog with an arrow pointing to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_edit_model_3.jpg)
+
+## Delete Model
+
+Navigate to the Model page of the model you want to delete, open the model actions dropdown and click on the **Delete** option. This action will delete the model.
+
+![Ultralytics HUB screenshot of the Model page with an arrow pointing to the Delete option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_delete_model_1.jpg)
+
+??? tip "Tip"
+
+    You can also delete a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
+
+    ![Ultralytics HUB screenshot of the Models page with an arrow pointing to the Delete option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_delete_model_2.jpg)
+
+??? note "Note"
+
+    If you change your mind, you can restore the model from the [Trash](https://hub.ultralytics.com/trash) page.
+
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_delete_model_3.jpg)

downloads/ultralytics-main/docs/hub/projects.md

@@ -1,7 +1,169 @@
 ---
 comments: true
+description: Learn how to manage Ultralytics HUB projects. Understand effective strategies to create, share, edit, delete, and compare models in an organized workspace.
+keywords: Ultralytics, HUB projects, Create project, Edit project, Share project, Delete project, Compare Models, Model Management
 ---
 
-# 🚧 Page Under Construction ⚒
+# Ultralytics HUB Projects
 
-This page is currently under construction!️ 👷Please check back later for updates. 😃🔜
+Ultralytics HUB projects provide an effective solution for consolidating and managing your models. If you are working with several models that perform similar tasks or have related purposes, Ultralytics HUB projects allow you to group these models together.
+
+This creates a unified and organized workspace that facilitates easier model management, comparison and development. Having similar models or various iterations together can facilitate rapid benchmarking, as you can compare their effectiveness. This can lead to faster, more insightful iterative development and refinement of your models.
+
+## Create Project
+
+Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar.
+
+![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Projects button in the sidebar](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also create a project directly from the [Home](https://hub.ultralytics.com/home) page.
+
+    ![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Create Project card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_2.jpg)
+
+Click on the **Create Project** button on the top right of the page. This action will trigger the **Create Project** dialog, opening up a suite of options for tailoring your project to your needs.
+
+![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Create Project button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_3.jpg)
+
+Type the name of your project in the _Project name_ field or keep the default name and finalize the project creation with a single click.
+
+You have the additional option to enrich your project with a description and a unique image, enhancing its recognizability on the Projects page.
+
+When you're happy with your project configuration, click **Create**.
+
+![Ultralytics HUB screenshot of the Create Project dialog with an arrow pointing to the Create button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_4.jpg)
+
+After your project is created, you will be able to access it from the Projects page.
+
+![Ultralytics HUB screenshot of the Projects page with an arrow pointing to one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_5.jpg)
+
+Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) inside your project.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Train Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_6.jpg)
+
+## Share Project
+
+!!! info "Info"
+
+    Ultralytics HUB's sharing functionality provides a convenient way to share projects with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
+
+??? note "Note"
+
+    You have control over the general access of your projects.
+
+    You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the project, regardless of whether they have an Ultralytics HUB account or not.
+
+Navigate to the Project page of the project you want to share, open the project actions dropdown and click on the **Share** option. This action will trigger the **Share Project** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Share option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also share a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Share option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_2.jpg)
+
+Set the general access to "Unlisted" and click **Save**.
+
+![Ultralytics HUB screenshot of the Share Project dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_3.jpg)
+
+!!! warning "Warning"
+
+    When changing the general access of a project, the general access of the models inside the project will be changed as well.
+
+Now, anyone who has the direct link to your project can view it.
+
+??? tip "Tip"
+
+    You can easily click on the project's link shown in the **Share Project** dialog to copy it.
+
+    ![Ultralytics HUB screenshot of the Share Project dialog with an arrow pointing to the project's link](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_4.jpg)
+
+## Edit Project
+
+Navigate to the Project page of the project you want to edit, open the project actions dropdown and click on the **Edit** option. This action will trigger the **Update Project** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Edit option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also edit a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Edit option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_2.jpg)
+
+Apply the desired modifications to your project and then confirm the changes by clicking **Save**.
+
+![Ultralytics HUB screenshot of the Update Project dialog with an arrow pointing to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_3.jpg)
+
+## Delete Project
+
+Navigate to the Project page of the project you want to delete, open the project actions dropdown and click on the **Delete** option. This action will delete the project.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Delete option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also delete a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Delete option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_2.jpg)
+
+!!! warning "Warning"
+
+    When deleting a project, the the models inside the project will be deleted as well.
+
+??? note "Note"
+
+    If you change your mind, you can restore the project from the [Trash](https://hub.ultralytics.com/trash) page.
+
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_3.jpg)
+
+## Compare Models
+
+Navigate to the Project page of the project where the models you want to compare are located. To use the model comparison feature, click on the **Charts** tab.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Charts tab](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_1.jpg)
+
+This will display all the relevant charts. Each chart corresponds to a different metric and contains the performance of each model for that metric. The models are represented by different colors and you can hover over each data point to get more information.
+
+![Ultralytics HUB screenshot of the Charts tab inside the Project page](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_2.jpg)
+
+??? tip "Tip"
+
+    Each chart can be enlarged for better visualization.
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with an arrow pointing to the expand icon](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_3.jpg)
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with one of the charts expanded](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_4.jpg)
+
+??? tip "Tip"
+
+    You have the flexibility to customize your view by selectively hiding certain models. This feature allows you to concentrate on the models of interest.
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with an arrow pointing to the hide/unhide icon of one of the model](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_5.jpg)
+
+## Reorder Models
+
+??? note "Note"
+
+    Ultralytics HUB's reordering functionality works only inside projects you own.
+
+Navigate to the Project page of the project where the models you want to reorder are located. Click on the designated reorder icon of the model you want to move and drag it to the desired location.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the reorder icon](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_reorder_models_1.jpg)
+
+## Transfer Models
+
+Navigate to the Project page of the project where the model you want to mode is located, open the project actions dropdown and click on the **Transfer** option. This action will trigger the **Transfer Model** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Transfer option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_1.jpg)
+
+??? tip "Tip"
+
+    You can also transfer a model directly from the [Models](https://hub.ultralytics.com/models) page.
+
+    ![Ultralytics HUB screenshot of the Models page with an arrow pointing to the Transfer option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_2.jpg)
+
+Select the project you want to transfer the model to and click **Save**.
+
+![Ultralytics HUB screenshot of the Transfer Model dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_3.jpg)

downloads/ultralytics-main/docs/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Explore Ultralytics YOLOv8, a cutting-edge real-time object detection and image segmentation model for various applications and hardware platforms.
+description: Explore a complete guide to Ultralytics YOLOv8, a high-speed, high-accuracy object detection & image segmentation model. Installation, prediction, training tutorials and more.
+keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine learning, deep learning, computer vision, YOLOv8 installation, YOLOv8 prediction, YOLOv8 training, YOLO history, YOLO licenses
 ---
 
 <div align="center">
@@ -9,6 +10,7 @@ description: Explore Ultralytics YOLOv8, a cutting-edge real-time object detecti
     <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
   </p>
   <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+  <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
   <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
   <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
   <br>
@@ -38,4 +40,13 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
 - [YOLOv5](https://github.com/ultralytics/yolov5) further improved the model's performance and added new features such as hyperparameter optimization, integrated experiment tracking and automatic export to popular export formats.
 - [YOLOv6](https://github.com/meituan/YOLOv6) was open-sourced by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
 - [YOLOv7](https://github.com/WongKinYiu/yolov7) added additional tasks such as pose estimation on the COCO keypoints dataset.
-- [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
+- [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
+
+## YOLO Licenses: How is Ultralytics YOLO licensed?
+
+Ultralytics offers two licensing options to accommodate diverse use cases:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
+- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license).
+
+Our licensing strategy is designed to ensure that any improvements to our open-source projects are returned to the community. We hold the principles of open source close to our hearts ❤️, and our mission is to guarantee that our contributions can be utilized and expanded upon in ways that are beneficial to all.

downloads/ultralytics-main/docs/integrations/index.md

@@ -0,0 +1,61 @@
+---
+comments: true
+description: Explore Ultralytics integrations with tools for dataset management, model optimization, ML workflows automation, experiment tracking, version control, and more. Learn about our support for various model export formats for deployment.
+keywords: Ultralytics integrations, Roboflow, Neural Magic, ClearML, Comet ML, DVC, Ultralytics HUB, MLFlow, Neptune, Ray Tune, TensorBoard, W&B, model export formats, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TF Lite, TF Edge TPU, TF.js, PaddlePaddle, NCNN
+---
+
+# Ultralytics Integrations
+
+Welcome to the Ultralytics Integrations page! This page provides an overview of our partnerships with various tools and platforms, designed to streamline your machine learning workflows, enhance dataset management, simplify model training, and facilitate efficient deployment.
+
+<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
+
+## Datasets Integrations
+
+- [Roboflow](https://roboflow.com/?ref=ultralytics): Facilitate seamless dataset management for Ultralytics models, offering robust annotation, preprocessing, and augmentation capabilities.
+
+## Training Integrations
+
+- [Comet ML](https://www.comet.ml/): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
+
+- [ClearML](https://clear.ml/): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.
+
+- [DVC](https://dvc.org/): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively.
+
+- [Ultralytics HUB](https://hub.ultralytics.com): Access and contribute to a community of pre-trained Ultralytics models.
+
+- [MLFlow](https://mlflow.org/): Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment.
+
+- [Neptune](https://neptune.ai/): Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps.
+
+- [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale.
+
+- [TensorBoard](https://tensorboard.dev/): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
+
+- [Weights & Biases (W&B)](https://wandb.ai/site): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.
+
+## Deployment Integrations
+
+- [Neural Magic](https://neuralmagic.com/): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
+
+### Export Formats
+
+We also support a variety of model export formats for deployment in different environments. Here are the available formats:
+
+| Format                                                             | `format` Argument | Model                     | Metadata | Arguments                                           |
+|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|
+| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
+| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
+| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
+| [OpenVINO](openvino.md)                                            | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`                                     |
+| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
+| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
+| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`                                    |
+| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`                                             |
+| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`                             |
+| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
+| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`                                             |
+| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
+| [NCNN](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
+
+Explore the links to learn more about each integration and how to get the most out of them with Ultralytics.

downloads/ultralytics-main/docs/integrations/openvino.md

@@ -0,0 +1,271 @@
+---
+comments: true
+description: Discover the power of deploying your Ultralytics YOLOv8 model using OpenVINO format for up to 10x speedup vs PyTorch.
+keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, OpenVINO, OpenVINO format
+---
+
+<img width="1024" src="https://user-images.githubusercontent.com/26833433/252345644-0cf84257-4b34-404c-b7ce-eb73dfbcaff1.png" alt="OpenVINO Ecosystem">
+
+**Export mode** is used for exporting a YOLOv8 model to a format that can be used for deployment. In this guide, we specifically cover exporting to OpenVINO, which can provide up to 3x [CPU](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_CPU.html) speedup as well as accelerating on other Intel hardware ([iGPU](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_GPU.html), [dGPU](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_GPU.html), [VPU](https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_supported_plugins_VPU.html), etc.).
+
+OpenVINO, short for Open Visual Inference & Neural Network Optimization toolkit, is a comprehensive toolkit for optimizing and deploying AI inference models. Even though the name contains Visual, OpenVINO also supports various additional tasks including language, audio, time series, etc.
+
+## Usage Examples
+
+Export a YOLOv8n model to OpenVINO format and run inference with the exported model.
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a YOLOv8n PyTorch model
+        model = YOLO('yolov8n.pt')
+
+        # Export the model
+        model.export(format='openvino')  # creates 'yolov8n_openvino_model/'
+
+        # Load the exported OpenVINO model
+        ov_model = YOLO('yolov8n_openvino_model/')
+
+        # Run inference
+        results = ov_model('https://ultralytics.com/images/bus.jpg')
+        ```
+    === "CLI"
+
+        ```bash
+        # Export a YOLOv8n PyTorch model to OpenVINO format
+        yolo export model=yolov8n.pt format=openvino  # creates 'yolov8n_openvino_model/'
+
+        # Run inference with the exported model
+        yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg'
+        ```
+
+## Arguments
+
+| Key      | Value        | Description                                          |
+|----------|--------------|------------------------------------------------------|
+| `format` | `'openvino'` | format to export to                                  |
+| `imgsz`  | `640`        | image size as scalar or (h, w) list, i.e. (640, 480) |
+| `half`   | `False`      | FP16 quantization                                    |
+
+## Benefits of OpenVINO
+
+1. **Performance**: OpenVINO delivers high-performance inference by utilizing the power of Intel CPUs, integrated and discrete GPUs, and FPGAs.
+2. **Support for Heterogeneous Execution**: OpenVINO provides an API to write once and deploy on any supported Intel hardware (CPU, GPU, FPGA, VPU, etc.).
+3. **Model Optimizer**: OpenVINO provides a Model Optimizer that imports, converts, and optimizes models from popular deep learning frameworks such as PyTorch, TensorFlow, TensorFlow Lite, Keras, ONNX, PaddlePaddle, and Caffe.
+4. **Ease of Use**: The toolkit comes with more than [80 tutorial notebooks](https://github.com/openvinotoolkit/openvino_notebooks) (including [YOLOv8 optimization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/230-yolov8-optimization)) teaching different aspects of the toolkit.
+
+## OpenVINO Export Structure
+
+When you export a model to OpenVINO format, it results in a directory containing the following:
+
+1. **XML file**: Describes the network topology.
+2. **BIN file**: Contains the weights and biases binary data.
+3. **Mapping file**: Holds mapping of original model output tensors to OpenVINO tensor names.
+
+You can use these files to run inference with the OpenVINO Inference Engine.
+
+## Using OpenVINO Export in Deployment
+
+Once you have the OpenVINO files, you can use the OpenVINO Runtime to run the model. The Runtime provides a unified API to inference across all supported Intel hardware. It also provides advanced capabilities like load balancing across Intel hardware and asynchronous execution. For more information on running the inference, refer to the [Inference with OpenVINO Runtime Guide](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_OV_Runtime_User_Guide.html).
+
+Remember, you'll need the XML and BIN files as well as any application-specific settings like input size, scale factor for normalization, etc., to correctly set up and use the model with the Runtime.
+
+In your deployment application, you would typically do the following steps:
+
+1. Initialize OpenVINO by creating `core = Core()`.
+2. Load the model using the `core.read_model()` method.
+3. Compile the model using the `core.compile_model()` function.
+4. Prepare the input (image, text, audio, etc.).
+5. Run inference using `compiled_model(input_data)`.
+
+For more detailed steps and code snippets, refer to the [OpenVINO documentation](https://docs.openvino.ai/) or [API tutorial](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/002-openvino-api/002-openvino-api.ipynb).
+
+## OpenVINO YOLOv8 Benchmarks
+
+YOLOv8 benchmarks below were run by the Ultralytics team on 4 different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX and OpenVINO. Benchmarks were run on Intel Flex and Arc GPUs, and on Intel Xeon CPUs at FP32 precision (with the `half=False` argument).
+
+!!! note
+
+    The benchmarking results below are for reference and might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run.
+
+    All benchmarks run with `openvino` python package version [2023.0.1](https://pypi.org/project/openvino/2023.0.1/).
+
+### Intel Flex GPU
+
+The Intel® Data Center GPU Flex Series is a versatile and robust solution designed for the intelligent visual cloud. This GPU supports a wide array of workloads including media streaming, cloud gaming, AI visual inference, and virtual desktop Infrastructure workloads. It stands out for its open architecture and built-in support for the AV1 encode, providing a standards-based software stack for high-performance, cross-architecture applications. The Flex Series GPU is optimized for density and quality, offering high reliability, availability, and scalability.
+
+Benchmarks below run on Intel® Data Center GPU Flex 170 at FP32 precision.
+
+<div align="center">
+<img width="800" src="https://user-images.githubusercontent.com/26833433/253741543-62659bf8-1765-4d0b-b71c-8a4f9885506a.jpg">
+</div>
+
+| Model   | Format      | Status | Size (MB) | mAP50-95(B) | Inference time (ms/im) |
+|---------|-------------|--------|-----------|-------------|------------------------|
+| YOLOv8n | PyTorch     | ✅      | 6.2       | 0.3709      | 21.79                  |
+| YOLOv8n | TorchScript | ✅      | 12.4      | 0.3704      | 23.24                  |
+| YOLOv8n | ONNX        | ✅      | 12.2      | 0.3704      | 37.22                  |
+| YOLOv8n | OpenVINO    | ✅      | 12.3      | 0.3703      | 3.29                   |
+| YOLOv8s | PyTorch     | ✅      | 21.5      | 0.4471      | 31.89                  |
+| YOLOv8s | TorchScript | ✅      | 42.9      | 0.4472      | 32.71                  |
+| YOLOv8s | ONNX        | ✅      | 42.8      | 0.4472      | 43.42                  |
+| YOLOv8s | OpenVINO    | ✅      | 42.9      | 0.4470      | 3.92                   |
+| YOLOv8m | PyTorch     | ✅      | 49.7      | 0.5013      | 50.75                  |
+| YOLOv8m | TorchScript | ✅      | 99.2      | 0.4999      | 47.90                  |
+| YOLOv8m | ONNX        | ✅      | 99.0      | 0.4999      | 63.16                  |
+| YOLOv8m | OpenVINO    | ✅      | 49.8      | 0.4997      | 7.11                   |
+| YOLOv8l | PyTorch     | ✅      | 83.7      | 0.5293      | 77.45                  |
+| YOLOv8l | TorchScript | ✅      | 167.2     | 0.5268      | 85.71                  |
+| YOLOv8l | ONNX        | ✅      | 166.8     | 0.5268      | 88.94                  |
+| YOLOv8l | OpenVINO    | ✅      | 167.0     | 0.5264      | 9.37                   |
+| YOLOv8x | PyTorch     | ✅      | 130.5     | 0.5404      | 100.09                 |
+| YOLOv8x | TorchScript | ✅      | 260.7     | 0.5371      | 114.64                 |
+| YOLOv8x | ONNX        | ✅      | 260.4     | 0.5371      | 110.32                 |
+| YOLOv8x | OpenVINO    | ✅      | 260.6     | 0.5367      | 15.02                  |
+
+This table represents the benchmark results for five different models (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) across four different formats (PyTorch, TorchScript, ONNX, OpenVINO), giving us the status, size, mAP50-95(B) metric, and inference time for each combination.
+
+### Intel Arc GPU
+
+Intel® Arc™ represents Intel's foray into the dedicated GPU market. The Arc™ series, designed to compete with leading GPU manufacturers like AMD and Nvidia, caters to both the laptop and desktop markets. The series includes mobile versions for compact devices like laptops, and larger, more powerful versions for desktop computers.
+
+The Arc™ series is divided into three categories: Arc™ 3, Arc™ 5, and Arc™ 7, with each number indicating the performance level. Each category includes several models, and the 'M' in the GPU model name signifies a mobile, integrated variant.
+
+Early reviews have praised the Arc™ series, particularly the integrated A770M GPU, for its impressive graphics performance. The availability of the Arc™ series varies by region, and additional models are expected to be released soon. Intel® Arc™ GPUs offer high-performance solutions for a range of computing needs, from gaming to content creation.
+
+Benchmarks below run on Intel® Arc 770 GPU at FP32 precision.
+
+<div align="center">
+<img width="800" src="https://user-images.githubusercontent.com/26833433/253741545-8530388f-8fd1-44f7-a4ae-f875d59dc282.jpg">
+</div>
+
+| Model   | Format      | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
+|---------|-------------|--------|-----------|---------------------|------------------------|
+| YOLOv8n | PyTorch     | ✅      | 6.2       | 0.3709              | 88.79                  |
+| YOLOv8n | TorchScript | ✅      | 12.4      | 0.3704              | 102.66                 |
+| YOLOv8n | ONNX        | ✅      | 12.2      | 0.3704              | 57.98                  |
+| YOLOv8n | OpenVINO    | ✅      | 12.3      | 0.3703              | 8.52                   |
+| YOLOv8s | PyTorch     | ✅      | 21.5      | 0.4471              | 189.83                 |
+| YOLOv8s | TorchScript | ✅      | 42.9      | 0.4472              | 227.58                 |
+| YOLOv8s | ONNX        | ✅      | 42.7      | 0.4472              | 142.03                 |
+| YOLOv8s | OpenVINO    | ✅      | 42.9      | 0.4469              | 9.19                   |
+| YOLOv8m | PyTorch     | ✅      | 49.7      | 0.5013              | 411.64                 |
+| YOLOv8m | TorchScript | ✅      | 99.2      | 0.4999              | 517.12                 |
+| YOLOv8m | ONNX        | ✅      | 98.9      | 0.4999              | 298.68                 |
+| YOLOv8m | OpenVINO    | ✅      | 99.1      | 0.4996              | 12.55                  |
+| YOLOv8l | PyTorch     | ✅      | 83.7      | 0.5293              | 725.73                 |
+| YOLOv8l | TorchScript | ✅      | 167.1     | 0.5268              | 892.83                 |
+| YOLOv8l | ONNX        | ✅      | 166.8     | 0.5268              | 576.11                 |
+| YOLOv8l | OpenVINO    | ✅      | 167.0     | 0.5262              | 17.62                  |
+| YOLOv8x | PyTorch     | ✅      | 130.5     | 0.5404              | 988.92                 |
+| YOLOv8x | TorchScript | ✅      | 260.7     | 0.5371              | 1186.42                |
+| YOLOv8x | ONNX        | ✅      | 260.4     | 0.5371              | 768.90                 |
+| YOLOv8x | OpenVINO    | ✅      | 260.6     | 0.5367              | 19                     |
+
+### Intel Xeon CPU
+
+The Intel® Xeon® CPU is a high-performance, server-grade processor designed for complex and demanding workloads. From high-end cloud computing and virtualization to artificial intelligence and machine learning applications, Xeon® CPUs provide the power, reliability, and flexibility required for today's data centers.
+
+Notably, Xeon® CPUs deliver high compute density and scalability, making them ideal for both small businesses and large enterprises. By choosing Intel® Xeon® CPUs, organizations can confidently handle their most demanding computing tasks and foster innovation while maintaining cost-effectiveness and operational efficiency.
+
+Benchmarks below run on 4th Gen Intel® Xeon® Scalable CPU at FP32 precision.
+
+<div align="center">
+<img width="800" src="https://user-images.githubusercontent.com/26833433/253741546-dcd8e52a-fc38-424f-b87e-c8365b6f28dc.jpg">
+</div>
+
+| Model   | Format      | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
+|---------|-------------|--------|-----------|---------------------|------------------------|
+| YOLOv8n | PyTorch     | ✅      | 6.2       | 0.3709              | 24.36                  |
+| YOLOv8n | TorchScript | ✅      | 12.4      | 0.3704              | 23.93                  |
+| YOLOv8n | ONNX        | ✅      | 12.2      | 0.3704              | 39.86                  |
+| YOLOv8n | OpenVINO    | ✅      | 12.3      | 0.3704              | 11.34                  |
+| YOLOv8s | PyTorch     | ✅      | 21.5      | 0.4471              | 33.77                  |
+| YOLOv8s | TorchScript | ✅      | 42.9      | 0.4472              | 34.84                  |
+| YOLOv8s | ONNX        | ✅      | 42.8      | 0.4472              | 43.23                  |
+| YOLOv8s | OpenVINO    | ✅      | 42.9      | 0.4471              | 13.86                  |
+| YOLOv8m | PyTorch     | ✅      | 49.7      | 0.5013              | 53.91                  |
+| YOLOv8m | TorchScript | ✅      | 99.2      | 0.4999              | 53.51                  |
+| YOLOv8m | ONNX        | ✅      | 99.0      | 0.4999              | 64.16                  |
+| YOLOv8m | OpenVINO    | ✅      | 99.1      | 0.4996              | 28.79                  |
+| YOLOv8l | PyTorch     | ✅      | 83.7      | 0.5293              | 75.78                  |
+| YOLOv8l | TorchScript | ✅      | 167.2     | 0.5268              | 79.13                  |
+| YOLOv8l | ONNX        | ✅      | 166.8     | 0.5268              | 88.45                  |
+| YOLOv8l | OpenVINO    | ✅      | 167.0     | 0.5263              | 56.23                  |
+| YOLOv8x | PyTorch     | ✅      | 130.5     | 0.5404              | 96.60                  |
+| YOLOv8x | TorchScript | ✅      | 260.7     | 0.5371              | 114.28                 |
+| YOLOv8x | ONNX        | ✅      | 260.4     | 0.5371              | 111.02                 |
+| YOLOv8x | OpenVINO    | ✅      | 260.6     | 0.5371              | 83.28                  |
+
+### Intel Core CPU
+
+The Intel® Core® series is a range of high-performance processors by Intel. The lineup includes Core i3 (entry-level), Core i5 (mid-range), Core i7 (high-end), and Core i9 (extreme performance). Each series caters to different computing needs and budgets, from everyday tasks to demanding professional workloads. With each new generation, improvements are made to performance, energy efficiency, and features.
+
+Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.
+
+<div align="center">
+<img width="800" src="https://user-images.githubusercontent.com/26833433/254559985-727bfa43-93fa-4fec-a417-800f869f3f9e.jpg">
+</div>
+
+| Model   | Format      | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
+|---------|-------------|--------|-----------|---------------------|------------------------|
+| YOLOv8n | PyTorch     | ✅      | 6.2       | 0.4478              | 104.61                 |
+| YOLOv8n | TorchScript | ✅      | 12.4      | 0.4525              | 112.39                 |
+| YOLOv8n | ONNX        | ✅      | 12.2      | 0.4525              | 28.02                  |
+| YOLOv8n | OpenVINO    | ✅      | 12.3      | 0.4504              | 23.53                  |
+| YOLOv8s | PyTorch     | ✅      | 21.5      | 0.5885              | 194.83                 |
+| YOLOv8s | TorchScript | ✅      | 43.0      | 0.5962              | 202.01                 |
+| YOLOv8s | ONNX        | ✅      | 42.8      | 0.5962              | 65.74                  |
+| YOLOv8s | OpenVINO    | ✅      | 42.9      | 0.5966              | 38.66                  |
+| YOLOv8m | PyTorch     | ✅      | 49.7      | 0.6101              | 355.23                 |
+| YOLOv8m | TorchScript | ✅      | 99.2      | 0.6120              | 424.78                 |
+| YOLOv8m | ONNX        | ✅      | 99.0      | 0.6120              | 173.39                 |
+| YOLOv8m | OpenVINO    | ✅      | 99.1      | 0.6091              | 69.80                  |
+| YOLOv8l | PyTorch     | ✅      | 83.7      | 0.6591              | 593.00                 |
+| YOLOv8l | TorchScript | ✅      | 167.2     | 0.6580              | 697.54                 |
+| YOLOv8l | ONNX        | ✅      | 166.8     | 0.6580              | 342.15                 |
+| YOLOv8l | OpenVINO    | ✅      | 167.0     | 0.0708              | 117.69                 |
+| YOLOv8x | PyTorch     | ✅      | 130.5     | 0.6651              | 804.65                 |
+| YOLOv8x | TorchScript | ✅      | 260.8     | 0.6650              | 921.46                 |
+| YOLOv8x | ONNX        | ✅      | 260.4     | 0.6650              | 526.66                 |
+| YOLOv8x | OpenVINO    | ✅      | 260.6     | 0.6619              | 158.73                 |
+
+## Reproduce Our Results
+
+To reproduce the Ultralytics benchmarks above on all export [formats](../modes/export.md) run this code:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a YOLOv8n PyTorch model
+        model = YOLO('yolov8n.pt')
+
+        # Benchmark YOLOv8n speed and accuracy on the COCO128 dataset for all all export formats
+        results= model.benchmarks(data='coco128.yaml')
+        ```
+    === "CLI"
+
+        ```bash
+        # Benchmark YOLOv8n speed and accuracy on the COCO128 dataset for all all export formats
+        yolo benchmark model=yolov8n.pt data=coco128.yaml
+        ```
+
+    Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results use a dataset with a large number of images, i.e. `data='coco128.yaml' (128 val images), or `data='coco.yaml'` (5000 val images).
+
+## Conclusion
+
+The benchmarking results clearly demonstrate the benefits of exporting the YOLOv8 model to the OpenVINO format. Across different models and hardware platforms, the OpenVINO format consistently outperforms other formats in terms of inference speed while maintaining comparable accuracy.
+
+For the Intel® Data Center GPU Flex Series, the OpenVINO format was able to deliver inference speeds almost 10 times faster than the original PyTorch format. On the Xeon CPU, the OpenVINO format was twice as fast as the PyTorch format. The accuracy of the models remained nearly identical across the different formats.
+
+The benchmarks underline the effectiveness of OpenVINO as a tool for deploying deep learning models. By converting models to the OpenVINO format, developers can achieve significant performance improvements, making it easier to deploy these models in real-world applications.
+
+For more detailed information and instructions on using OpenVINO, refer to the [official OpenVINO documentation](https://docs.openvinotoolkit.org/latest/index.html).

downloads/ultralytics-main/docs/integrations/ray-tune.md

@@ -0,0 +1,174 @@
+---
+comments: true
+description: Discover how to streamline hyperparameter tuning for YOLOv8 models with Ray Tune. Learn to accelerate tuning, integrate with Weights & Biases, and analyze results.
+keywords: Ultralytics, YOLOv8, Ray Tune, hyperparameter tuning, machine learning optimization, Weights & Biases integration, result analysis
+---
+
+# Efficient Hyperparameter Tuning with Ray Tune and YOLOv8
+
+Hyperparameter tuning is vital in achieving peak model performance by discovering the optimal set of hyperparameters. This involves running trials with different hyperparameters and evaluating each trial’s performance.
+
+## Accelerate Tuning with Ultralytics YOLOv8 and Ray Tune
+
+[Ultralytics YOLOv8](https://ultralytics.com) incorporates Ray Tune for hyperparameter tuning, streamlining the optimization of YOLOv8 model hyperparameters. With Ray Tune, you can utilize advanced search strategies, parallelism, and early stopping to expedite the tuning process.
+
+### Ray Tune
+
+<p align="center">
+  <img width="640" src="https://docs.ray.io/en/latest/_images/tune_overview.png" alt="Ray Tune Overview">
+</p>
+
+[Ray Tune](https://docs.ray.io/en/latest/tune/index.html) is a hyperparameter tuning library designed for efficiency and flexibility. It supports various search strategies, parallelism, and early stopping strategies, and seamlessly integrates with popular machine learning frameworks, including Ultralytics YOLOv8.
+
+### Integration with Weights & Biases
+
+YOLOv8 also allows optional integration with [Weights & Biases](https://wandb.ai/site) for monitoring the tuning process.
+
+## Installation
+
+To install the required packages, run:
+
+!!! tip "Installation"
+
+    ```bash
+    # Install and update Ultralytics and Ray Tune packages
+    pip install -U ultralytics "ray[tune]"
+
+    # Optionally install W&B for logging
+    pip install wandb
+    ```
+
+## Usage
+
+!!! example "Usage"
+
+    ```python
+    from ultralytics import YOLO
+
+    # Load a YOLOv8n model
+    model = YOLO("yolov8n.pt")
+    
+    # Start tuning hyperparameters for YOLOv8n training on the COCO128 dataset
+    result_grid = model.tune(data="coco128.yaml")
+    ```
+
+## `tune()` Method Parameters
+
+The `tune()` method in YOLOv8 provides an easy-to-use interface for hyperparameter tuning with Ray Tune. It accepts several arguments that allow you to customize the tuning process. Below is a detailed explanation of each parameter:
+
+| Parameter       | Type             | Description                                                                                                                                                                                                                                                                                    | Default Value |
+|-----------------|------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
+| `data`          | `str`            | The dataset configuration file (in YAML format) to run the tuner on. This file should specify the training and validation data paths, as well as other dataset-specific settings.                                                                                                              |               |
+| `space`         | `dict, optional` | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLOv8 uses a default search space with various hyperparameters.                     |               |
+| `grace_period`  | `int, optional`  | The grace period in epochs for the [ASHA scheduler](https://docs.ray.io/en/latest/tune/api/schedulers.html) in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping. | 10            |
+| `gpu_per_trial` | `int, optional`  | The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs.                                                                                                           | None          |
+| `max_samples`   | `int, optional`  | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely.                                                                                                 | 10            |
+| `**train_args`  | `dict, optional` | Additional arguments to pass to the `train()` method during tuning. These arguments can include settings like the number of training epochs, batch size, and other training-specific configurations.                                                                                           | {}            |
+
+By customizing these parameters, you can fine-tune the hyperparameter optimization process to suit your specific needs and available computational resources.
+
+## Default Search Space Description
+
+The following table lists the default search space parameters for hyperparameter tuning in YOLOv8 with Ray Tune. Each parameter has a specific value range defined by `tune.uniform()`.
+
+| Parameter         | Value Range                | Description                              |
+|-------------------|----------------------------|------------------------------------------|
+| `lr0`             | `tune.uniform(1e-5, 1e-1)` | Initial learning rate                    |
+| `lrf`             | `tune.uniform(0.01, 1.0)`  | Final learning rate factor               |
+| `momentum`        | `tune.uniform(0.6, 0.98)`  | Momentum                                 |
+| `weight_decay`    | `tune.uniform(0.0, 0.001)` | Weight decay                             |
+| `warmup_epochs`   | `tune.uniform(0.0, 5.0)`   | Warmup epochs                            |
+| `warmup_momentum` | `tune.uniform(0.0, 0.95)`  | Warmup momentum                          |
+| `box`             | `tune.uniform(0.02, 0.2)`  | Box loss weight                          |
+| `cls`             | `tune.uniform(0.2, 4.0)`   | Class loss weight                        |
+| `hsv_h`           | `tune.uniform(0.0, 0.1)`   | Hue augmentation range                   |
+| `hsv_s`           | `tune.uniform(0.0, 0.9)`   | Saturation augmentation range            |
+| `hsv_v`           | `tune.uniform(0.0, 0.9)`   | Value (brightness) augmentation range    |
+| `degrees`         | `tune.uniform(0.0, 45.0)`  | Rotation augmentation range (degrees)    |
+| `translate`       | `tune.uniform(0.0, 0.9)`   | Translation augmentation range           |
+| `scale`           | `tune.uniform(0.0, 0.9)`   | Scaling augmentation range               |
+| `shear`           | `tune.uniform(0.0, 10.0)`  | Shear augmentation range (degrees)       |
+| `perspective`     | `tune.uniform(0.0, 0.001)` | Perspective augmentation range           |
+| `flipud`          | `tune.uniform(0.0, 1.0)`   | Vertical flip augmentation probability   |
+| `fliplr`          | `tune.uniform(0.0, 1.0)`   | Horizontal flip augmentation probability |
+| `mosaic`          | `tune.uniform(0.0, 1.0)`   | Mosaic augmentation probability          |
+| `mixup`           | `tune.uniform(0.0, 1.0)`   | Mixup augmentation probability           |
+| `copy_paste`      | `tune.uniform(0.0, 1.0)`   | Copy-paste augmentation probability      |
+
+## Custom Search Space Example
+
+In this example, we demonstrate how to use a custom search space for hyperparameter tuning with Ray Tune and YOLOv8. By providing a custom search space, you can focus the tuning process on specific hyperparameters of interest.
+
+!!! example "Usage"
+
+    ```python
+    from ultralytics import YOLO
+
+    # Define a YOLO model
+    model = YOLO("yolov8n.pt")
+
+    # Run Ray Tune on the model
+    result_grid = model.tune(data="coco128.yaml",
+                             space={"lr0": tune.uniform(1e-5, 1e-1)},
+                             epochs=50)
+    ```
+
+In the code snippet above, we create a YOLO model with the "yolov8n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco128.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`.
+
+# Processing Ray Tune Results
+
+After running a hyperparameter tuning experiment with Ray Tune, you might want to perform various analyses on the obtained results. This guide will take you through common workflows for processing and analyzing these results.
+
+## Loading Tune Experiment Results from a Directory
+
+After running the tuning experiment with `tuner.fit()`, you can load the results from a directory. This is useful, especially if you're performing the analysis after the initial training script has exited.
+
+```python
+experiment_path = f"{storage_path}/{exp_name}"
+print(f"Loading results from {experiment_path}...")
+
+restored_tuner = tune.Tuner.restore(experiment_path, trainable=train_mnist)
+result_grid = restored_tuner.get_results()
+```
+
+## Basic Experiment-Level Analysis
+
+Get an overview of how trials performed. You can quickly check if there were any errors during the trials.
+
+```python
+if result_grid.errors:
+    print("One or more trials failed!")
+else:
+    print("No errors!")
+```
+
+## Basic Trial-Level Analysis
+
+Access individual trial hyperparameter configurations and the last reported metrics.
+
+```python
+for i, result in enumerate(result_grid):
+    print(f"Trial #{i}: Configuration: {result.config}, Last Reported Metrics: {result.metrics}")
+```
+
+## Plotting the Entire History of Reported Metrics for a Trial
+
+You can plot the history of reported metrics for each trial to see how the metrics evolved over time.
+
+```python
+import matplotlib.pyplot as plt
+
+for result in result_grid:
+    plt.plot(result.metrics_dataframe["training_iteration"], result.metrics_dataframe["mean_accuracy"], label=f"Trial {i}")
+
+plt.xlabel('Training Iterations')
+plt.ylabel('Mean Accuracy')
+plt.legend()
+plt.show()
+```
+
+## Summary
+
+In this documentation, we covered common workflows to analyze the results of experiments run with Ray Tune using Ultralytics. The key steps include loading the experiment results from a directory, performing basic experiment-level and trial-level analysis and plotting metrics.
+
+Explore further by looking into Ray Tune’s [Analyze Results](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html) docs page to get the most out of your hyperparameter tuning experiments.

downloads/ultralytics-main/docs/models/fast-sam.md

@@ -0,0 +1,186 @@
+---
+comments: true
+description: Explore FastSAM, a CNN-based solution for real-time object segmentation in images. Enhanced user interaction, computational efficiency and adaptable across vision tasks.
+keywords: FastSAM, machine learning, CNN-based solution, object segmentation, real-time solution, Ultralytics, vision tasks, image processing, industrial applications, user interaction
+---
+
+# Fast Segment Anything Model (FastSAM)
+
+The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based solution for the Segment Anything task. This task is designed to segment any object within an image based on various possible user interaction prompts. FastSAM significantly reduces computational demands while maintaining competitive performance, making it a practical choice for a variety of vision tasks.
+
+![Fast Segment Anything Model (FastSAM) architecture overview](https://user-images.githubusercontent.com/26833433/248551984-d98f0f6d-7535-45d0-b380-2e1440b52ad7.jpg)
+
+## Overview
+
+FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
+
+## Key Features
+
+1. **Real-time Solution:** By leveraging the computational efficiency of CNNs, FastSAM provides a real-time solution for the segment anything task, making it valuable for industrial applications that require quick results.
+
+2. **Efficiency and Performance:** FastSAM offers a significant reduction in computational and resource demands without compromising on performance quality. It achieves comparable performance to SAM but with drastically reduced computational resources, enabling real-time application.
+
+3. **Prompt-guided Segmentation:** FastSAM can segment any object within an image guided by various possible user interaction prompts, providing flexibility and adaptability in different scenarios.
+
+4. **Based on YOLOv8-seg:** FastSAM is based on [YOLOv8-seg](../tasks/segment.md), an object detector equipped with an instance segmentation branch. This allows it to effectively produce the segmentation masks of all instances in an image.
+
+5. **Competitive Results on Benchmarks:** On the object proposal task on MS COCO, FastSAM achieves high scores at a significantly faster speed than [SAM](sam.md) on a single NVIDIA RTX 3090, demonstrating its efficiency and capability.
+
+6. **Practical Applications:** The proposed approach provides a new, practical solution for a large number of vision tasks at a really high speed, tens or hundreds of times faster than current methods.
+
+7. **Model Compression Feasibility:** FastSAM demonstrates the feasibility of a path that can significantly reduce the computational effort by introducing an artificial prior to the structure, thus opening new possibilities for large model architecture for general vision tasks.
+
+## Usage
+
+### Python API
+
+The FastSAM models are easy to integrate into your Python applications. Ultralytics provides a user-friendly Python API to streamline the process.
+
+#### Predict Usage
+
+To perform object detection on an image, use the `predict` method as shown below:
+
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import FastSAM
+        from ultralytics.models.fastsam import FastSAMPrompt
+
+        # Define an inference source
+        source = 'path/to/bus.jpg'
+
+        # Create a FastSAM model
+        model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
+
+        # Run inference on an image
+        everything_results = model(source, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
+      
+        # Prepare a Prompt Process object
+        prompt_process = FastSAMPrompt(source, everything_results, device='cpu')
+
+        # Everything prompt
+        ann = prompt_process.everything_prompt()
+
+        # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
+        ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
+
+        # Text prompt
+        ann = prompt_process.text_prompt(text='a photo of a dog')
+
+        # Point prompt
+        # points default [[0,0]] [[x1,y1],[x2,y2]]
+        # point_label default [0] [1,0] 0:background, 1:foreground
+        ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
+        prompt_process.plot(annotations=ann, output='./')
+        ```
+      
+    === "CLI"
+        ```bash
+        # Load a FastSAM model and segment everything with it
+        yolo segment predict model=FastSAM-s.pt source=path/to/bus.jpg imgsz=640
+        ```
+
+This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
+
+#### Val Usage
+
+Validation of the model on a dataset can be done as follows:
+
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import FastSAM
+
+        # Create a FastSAM model
+        model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
+
+        # Validate the model
+        results = model.val(data='coco8-seg.yaml')
+        ```
+
+    === "CLI"
+        ```bash
+        # Load a FastSAM model and validate it on the COCO8 example dataset at image size 640
+        yolo segment val model=FastSAM-s.pt data=coco8.yaml imgsz=640
+        ```
+
+Please note that FastSAM only supports detection and segmentation of a single class of object. This means it will recognize and segment all objects as the same class. Therefore, when preparing the dataset, you need to convert all object category IDs to 0.
+
+### FastSAM official Usage
+
+FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) repository. Here is a brief overview of the typical steps you might take to use FastSAM:
+
+#### Installation
+
+1. Clone the FastSAM repository:
+   ```shell
+   git clone https://github.com/CASIA-IVA-Lab/FastSAM.git
+   ```
+
+2. Create and activate a Conda environment with Python 3.9:
+   ```shell
+   conda create -n FastSAM python=3.9
+   conda activate FastSAM
+   ```
+
+3. Navigate to the cloned repository and install the required packages:
+   ```shell
+   cd FastSAM
+   pip install -r requirements.txt
+   ```
+
+4. Install the CLIP model:
+   ```shell
+   pip install git+https://github.com/openai/CLIP.git
+   ```
+
+#### Example Usage
+
+1. Download a [model checkpoint](https://drive.google.com/file/d/1m1sjY4ihXBU1fZXdQ-Xdj-mDltW-2Rqv/view?usp=sharing).
+
+2. Use FastSAM for inference. Example commands:
+
+    - Segment everything in an image:
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg
+      ```
+
+    - Segment specific objects using text prompt:
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
+      ```
+
+    - Segment objects within a bounding box (provide box coordinates in xywh format):
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
+      ```
+
+    - Segment objects near specific points:
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]"
+      ```
+
+Additionally, you can try FastSAM through a [Colab demo](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) or on the [HuggingFace web demo](https://huggingface.co/spaces/An-619/FastSAM) for a visual experience.
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the FastSAM authors for their significant contributions in the field of real-time instance segmentation:
+
+!!! note ""
+
+    === "BibTeX"
+
+      ```bibtex
+      @misc{zhao2023fast,
+            title={Fast Segment Anything},
+            author={Xu Zhao and Wenchao Ding and Yongqi An and Yinglong Du and Tao Yu and Min Li and Ming Tang and Jinqiao Wang},
+            year={2023},
+            eprint={2306.12156},
+            archivePrefix={arXiv},
+            primaryClass={cs.CV}
+      }
+      ```
+
+The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

downloads/ultralytics-main/docs/models/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics.
+description: Learn about the YOLO family, SAM, MobileSAM, FastSAM, YOLO-NAS, and RT-DETR models supported by Ultralytics, with examples on how to use them via CLI and Python.
+keywords: Ultralytics, documentation, YOLO, SAM, MobileSAM, FastSAM, YOLO-NAS, RT-DETR, models, architectures, Python, CLI
 ---
 
 # Models
@@ -9,29 +10,56 @@ Ultralytics supports many models and architectures with more to come in the futu
 
 In this documentation, we provide information on four major models:
 
-1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
-2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
-3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
-4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
-5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model.
+1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family originally by Joseph Redmon, known for its efficient real-time object detection capabilities.
+2. [YOLOv4](./yolov3.md): A darknet-native update to YOLOv3 released by Alexey Bochkovskiy in 2020.
+3. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture by Ultralytics, offering better performance and speed tradeoffs compared to previous versions.
+4. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
+5. [YOLOv7](./yolov7.md): Updated YOLO models released in 2022 by the authors of YOLOv4.
+6. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
+7. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
+8. [Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md): MobileSAM for mobile applications by Kyung Hee University.
+9. [Fast Segment Anything Model (FastSAM)](./fast-sam.md): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
+10. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
+11. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
 
-You can use these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
+You can use many of these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
 
-## CLI Example
+## Usage
 
-```bash
-yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
-```
+This example provides simple inference code for YOLO, SAM and RTDETR models. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using models with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
-## Python Example
+!!! example ""
 
-```python
-from ultralytics import YOLO
+    === "Python"
 
-model = YOLO("model.yaml")  # build a YOLOv8n model from scratch
-# YOLO("model.pt")  use pre-trained model if available
-model.info()  # display model information
-model.train(data="coco128.yaml", epochs=100)  # train the model
-```
+        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in python:
 
-For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a COCO-pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the YOLOv8n model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        CLI commands are available to directly run the models:
+
+        ```bash
+        # Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640
+
+        # Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image
+        yolo predict model=yolov8n.pt source=path/to/bus.jpg
+        ```
+
+For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.

downloads/ultralytics-main/docs/models/mobile-sam.md

@@ -0,0 +1,109 @@
+---
+comments: true
+description: Learn more about MobileSAM, its implementation, comparison with the original SAM, and how to download and test it in the Ultralytics framework. Improve your mobile applications today.
+keywords: MobileSAM, Ultralytics, SAM, mobile applications, Arxiv, GPU, API, image encoder, mask decoder, model download, testing method
+---
+
+![MobileSAM Logo](https://github.com/ChaoningZhang/MobileSAM/blob/master/assets/logo2.png?raw=true)
+
+# Mobile Segment Anything (MobileSAM)
+
+The MobileSAM paper is now available on [arXiv](https://arxiv.org/pdf/2306.14289.pdf).
+
+A demonstration of MobileSAM running on a CPU can be accessed at this [demo link](https://huggingface.co/spaces/dhkim2810/MobileSAM). The performance on a Mac i5 CPU takes approximately 3 seconds. On the Hugging Face demo, the interface and lower-performance CPUs contribute to a slower response, but it continues to function effectively.
+
+MobileSAM is implemented in various projects including [Grounding-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything), [AnyLabeling](https://github.com/vietanhdev/anylabeling), and [Segment Anything in 3D](https://github.com/Jumpat/SegmentAnythingin3D).
+
+MobileSAM is trained on a single GPU with a 100k dataset (1% of the original images) in less than a day. The code for this training will be made available in the future.
+
+## Adapting from SAM to MobileSAM
+
+Since MobileSAM retains the same pipeline as the original SAM, we have incorporated the original's pre-processing, post-processing, and all other interfaces. Consequently, those currently using the original SAM can transition to MobileSAM with minimal effort.
+
+MobileSAM performs comparably to the original SAM and retains the same pipeline except for a change in the image encoder. Specifically, we replace the original heavyweight ViT-H encoder (632M) with a smaller Tiny-ViT (5M). On a single GPU, MobileSAM operates at about 12ms per image: 8ms on the image encoder and 4ms on the mask decoder.
+
+The following table provides a comparison of ViT-based image encoders:
+
+| Image Encoder | Original SAM | MobileSAM |
+|---------------|--------------|-----------|
+| Parameters    | 611M         | 5M        |
+| Speed         | 452ms        | 8ms       |
+
+Both the original SAM and MobileSAM utilize the same prompt-guided mask decoder:
+
+| Mask Decoder | Original SAM | MobileSAM |
+|--------------|--------------|-----------|
+| Parameters   | 3.876M       | 3.876M    |
+| Speed        | 4ms          | 4ms       |
+
+Here is the comparison of the whole pipeline:
+
+| Whole Pipeline (Enc+Dec) | Original SAM | MobileSAM |
+|--------------------------|--------------|-----------|
+| Parameters               | 615M         | 9.66M     |
+| Speed                    | 456ms        | 12ms      |
+
+The performance of MobileSAM and the original SAM are demonstrated using both a point and a box as prompts.
+
+![Image with Point as Prompt](https://raw.githubusercontent.com/ChaoningZhang/MobileSAM/master/assets/mask_box.jpg?raw=true)
+
+![Image with Box as Prompt](https://raw.githubusercontent.com/ChaoningZhang/MobileSAM/master/assets/mask_box.jpg?raw=true)
+
+With its superior performance, MobileSAM is approximately 5 times smaller and 7 times faster than the current FastSAM. More details are available at the [MobileSAM project page](https://github.com/ChaoningZhang/MobileSAM).
+
+## Testing MobileSAM in Ultralytics
+
+Just like the original SAM, we offer a straightforward testing method in Ultralytics, including modes for both Point and Box prompts.
+
+### Model Download
+
+You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blob/master/weights/mobile_sam.pt).
+
+### Point Prompt
+
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import SAM
+        
+        # Load the model
+        model = SAM('mobile_sam.pt')
+        
+        # Predict a segment based on a point prompt
+        model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
+        ```
+
+### Box Prompt
+
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import SAM
+        
+        # Load the model
+        model = SAM('mobile_sam.pt')
+        
+        # Predict a segment based on a box prompt
+        model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
+        ```
+
+We have implemented `MobileSAM` and `SAM` using the same API. For more usage information, please see the [SAM page](./sam.md).
+
+## Citations and Acknowledgements
+
+If you find MobileSAM useful in your research or development work, please consider citing our paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{mobile_sam,
+          title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
+          author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung Ho and Lee, Seungkyu and Hong, Choong Seon},
+          journal={arXiv preprint arXiv:2306.14289},
+          year={2023}
+        }
+        ```

downloads/ultralytics-main/docs/models/rtdetr.md

@@ -1,44 +1,66 @@
 ---
 comments: true
-description: Explore RT-DETR, a high-performance real-time object detector. Learn how to use pre-trained models with Ultralytics Python API for various tasks.
+description: Discover the features and benefits of RT-DETR, Baidu’s efficient and adaptable real-time object detector powered by Vision Transformers, including pre-trained models.
+keywords: RT-DETR, Baidu, Vision Transformers, object detection, real-time performance, CUDA, TensorRT, IoU-aware query selection, Ultralytics, Python API, PaddlePaddle
 ---
 
-# RT-DETR
+# Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
 
 ## Overview
 
-Real-Time Detection Transformer (RT-DETR) is an end-to-end object detector that provides real-time performance while maintaining high accuracy. It efficiently processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion, and supports flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR outperforms many real-time object detectors on accelerated backends like CUDA with TensorRT.
+Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high accuracy. It leverages the power of Vision Transformers (ViT) to efficiently process multiscale features by decoupling intra-scale interaction and cross-scale fusion. RT-DETR is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. The model excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
 
 ![Model example image](https://user-images.githubusercontent.com/26833433/238963168-90e8483f-90aa-4eb6-a5e1-0d408b23dd33.png)
-**Overview of RT-DETR.** Model architecture diagram showing the last three stages of the backbone {S3, S4, S5} as the input
-to the encoder. The efficient hybrid encoder transforms multiscale features into a sequence of image features through intrascale feature interaction (AIFI) and cross-scale feature-fusion module (CCFM). The IoU-aware query selection is employed
-to select a fixed number of image features to serve as initial object queries for the decoder. Finally, the decoder with auxiliary
-prediction heads iteratively optimizes object queries to generate boxes and confidence scores ([source](https://arxiv.org/pdf/2304.08069.pdf)).
+**Overview of Baidu's RT-DETR.** The RT-DETR model architecture diagram shows the last three stages of the backbone {S3, S4, S5} as the input to the encoder. The efficient hybrid encoder transforms multiscale features into a sequence of image features through intrascale feature interaction (AIFI) and cross-scale feature-fusion module (CCFM). The IoU-aware query selection is employed to select a fixed number of image features to serve as initial object queries for the decoder. Finally, the decoder with auxiliary prediction heads iteratively optimizes object queries to generate boxes and confidence scores ([source](https://arxiv.org/pdf/2304.08069.pdf)).
 
 ### Key Features
 
-- **Efficient Hybrid Encoder:** RT-DETR uses an efficient hybrid encoder that processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion. This design reduces computational costs and allows for real-time object detection.
-- **IoU-aware Query Selection:** RT-DETR improves object query initialization by utilizing IoU-aware query selection. This allows the model to focus on the most relevant objects in the scene.
-- **Adaptable Inference Speed:** RT-DETR supports flexible adjustments of inference speed by using different decoder layers without the need for retraining. This adaptability facilitates practical application in various real-time object detection scenarios.
+- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. This unique Vision Transformers-based design reduces computational costs and allows for real-time object detection.
+- **IoU-aware Query Selection:** Baidu's RT-DETR improves object query initialization by utilizing IoU-aware query selection. This allows the model to focus on the most relevant objects in the scene, enhancing the detection accuracy.
+- **Adaptable Inference Speed:** Baidu's RT-DETR supports flexible adjustments of inference speed by using different decoder layers without the need for retraining. This adaptability facilitates practical application in various real-time object detection scenarios.
 
 ## Pre-trained Models
 
-Ultralytics RT-DETR provides several pre-trained models with different scales:
+The Ultralytics Python API provides pre-trained PaddlePaddle RT-DETR models with different scales:
 
 - RT-DETR-L: 53.0% AP on COCO val2017, 114 FPS on T4 GPU
 - RT-DETR-X: 54.8% AP on COCO val2017, 74 FPS on T4 GPU
 
 ## Usage
 
-### Python API
+You can use RT-DETR for object detection tasks using the `ultralytics` pip package. The following is a sample code snippet showing how to use RT-DETR models for training and inference:
 
-```python
-from ultralytics import RTDETR
+!!! example ""
 
-model = RTDETR("rtdetr-l.pt")
-model.info()  # display model information
-model.predict("path/to/image.jpg")  # predict
-```
+    This example provides simple inference code for RT-DETR. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using RT-DETR with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+
+    === "Python"
+
+        ```python
+        from ultralytics import RTDETR
+
+        # Load a COCO-pretrained RT-DETR-l model
+        model = RTDETR('rtdetr-l.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the RT-DETR-l model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        ```bash        
+        # Load a COCO-pretrained RT-DETR-l model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=rtdetr-l.pt data=coco8.yaml epochs=100 imgsz=640
+
+        # Load a COCO-pretrained RT-DETR-l model and run inference on the 'bus.jpg' image
+        yolo predict model=rtdetr-l.pt source=path/to/bus.jpg
+        ```
 
 ### Supported Tasks
 
@@ -53,21 +75,27 @@ model.predict("path/to/image.jpg")  # predict
 |------------|--------------------|
 | Inference  | :heavy_check_mark: |
 | Validation | :heavy_check_mark: |
-| Training   | :x: (Coming soon)  |
+| Training   | :heavy_check_mark: |
 
-# Citations and Acknowledgements
+## Citations and Acknowledgements
 
-If you use RT-DETR in your research or development work, please cite the [original paper](https://arxiv.org/abs/2304.08069):
+If you use Baidu's RT-DETR in your research or development work, please cite the [original paper](https://arxiv.org/abs/2304.08069):
 
-```bibtex
-@misc{lv2023detrs,
-      title={DETRs Beat YOLOs on Real-time Object Detection},
-      author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
-      year={2023},
-      eprint={2304.08069},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
 
-We would like to acknowledge Baidu's [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community.
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lv2023detrs,
+              title={DETRs Beat YOLOs on Real-time Object Detection},
+              author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
+              year={2023},
+              eprint={2304.08069},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
+
+*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*

downloads/ultralytics-main/docs/models/sam.md

@@ -1,46 +1,135 @@
 ---
 comments: true
-description: Learn about the Segment Anything Model (SAM) and how it provides promptable image segmentation through an advanced architecture and the SA-1B dataset.
+description: Explore the cutting-edge Segment Anything Model (SAM) from Ultralytics that allows real-time image segmentation. Learn about its promptable segmentation, zero-shot performance, and how to use it.
+keywords: Ultralytics, image segmentation, Segment Anything Model, SAM, SA-1B dataset, real-time performance, zero-shot transfer, object detection, image analysis, machine learning
 ---
 
 # Segment Anything Model (SAM)
 
-## Overview
+Welcome to the frontier of image segmentation with the Segment Anything Model, or SAM. This revolutionary model has changed the game by introducing promptable image segmentation with real-time performance, setting new standards in the field.
 
-The Segment Anything Model (SAM) is a groundbreaking image segmentation model that enables promptable segmentation with real-time performance. It forms the foundation for the Segment Anything project, which introduces a new task, model, and dataset for image segmentation. SAM is designed to be promptable, allowing it to transfer zero-shot to new image distributions and tasks. The model is trained on the [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/), which contains over 1 billion masks on 11 million licensed and privacy-respecting images. SAM has demonstrated impressive zero-shot performance, often surpassing prior fully supervised results.
+## Introduction to SAM: The Segment Anything Model
+
+The Segment Anything Model, or SAM, is a cutting-edge image segmentation model that allows for promptable segmentation, providing unparalleled versatility in image analysis tasks. SAM forms the heart of the Segment Anything initiative, a groundbreaking project that introduces a novel model, task, and dataset for image segmentation.
+
+SAM's advanced design allows it to adapt to new image distributions and tasks without prior knowledge, a feature known as zero-shot transfer. Trained on the expansive [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/), which contains more than 1 billion masks spread over 11 million carefully curated images, SAM has displayed impressive zero-shot performance, surpassing previous fully supervised results in many cases.
 
 ![Dataset sample image](https://user-images.githubusercontent.com/26833433/238056229-0e8ffbeb-f81a-477e-a490-aff3d82fd8ce.jpg)
 Example images with overlaid masks from our newly introduced dataset, SA-1B. SA-1B contains 11M diverse, high-resolution, licensed, and privacy protecting images and 1.1B high-quality segmentation masks. These masks were annotated fully automatically by SAM, and as verified by human ratings and numerous experiments, are of high quality and diversity. Images are grouped by number of masks per image for visualization (there are ∼100 masks per image on average).
 
-## Key Features
+## Key Features of the Segment Anything Model (SAM)
 
-- **Promptable Segmentation Task:** SAM is designed for a promptable segmentation task, enabling it to return a valid segmentation mask given any segmentation prompt, such as spatial or text information identifying an object.
-- **Advanced Architecture:** SAM utilizes a powerful image encoder, a prompt encoder, and a lightweight mask decoder. This architecture enables flexible prompting, real-time mask computation, and ambiguity awareness in segmentation.
-- **SA-1B Dataset:** The Segment Anything project introduces the SA-1B dataset, which contains over 1 billion masks on 11 million images. This dataset is the largest segmentation dataset to date, providing SAM with a diverse and large-scale source of data for training.
-- **Zero-Shot Performance:** SAM demonstrates remarkable zero-shot performance across a range of segmentation tasks, allowing it to be used out-of-the-box with prompt engineering for various applications.
+- **Promptable Segmentation Task:** SAM was designed with a promptable segmentation task in mind, allowing it to generate valid segmentation masks from any given prompt, such as spatial or text clues identifying an object.
+- **Advanced Architecture:** The Segment Anything Model employs a powerful image encoder, a prompt encoder, and a lightweight mask decoder. This unique architecture enables flexible prompting, real-time mask computation, and ambiguity awareness in segmentation tasks.
+- **The SA-1B Dataset:** Introduced by the Segment Anything project, the SA-1B dataset features over 1 billion masks on 11 million images. As the largest segmentation dataset to date, it provides SAM with a diverse and large-scale training data source.
+- **Zero-Shot Performance:** SAM displays outstanding zero-shot performance across various segmentation tasks, making it a ready-to-use tool for diverse applications with minimal need for prompt engineering.
 
-For more information about the Segment Anything Model and the SA-1B dataset, please refer to the [Segment Anything website](https://segment-anything.com) and the research paper [Segment Anything](https://arxiv.org/abs/2304.02643).
+For an in-depth look at the Segment Anything Model and the SA-1B dataset, please visit the [Segment Anything website](https://segment-anything.com) and check out the research paper [Segment Anything](https://arxiv.org/abs/2304.02643).
 
-## Usage
+## How to Use SAM: Versatility and Power in Image Segmentation
 
-SAM can be used for a variety of downstream tasks involving object and image distributions beyond its training data. Examples include edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. By employing prompt engineering, SAM can adapt to new tasks and data distributions in a zero-shot manner, making it a versatile and powerful tool for image segmentation tasks.
+The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs.
 
-```python
-from ultralytics.vit import SAM
+### SAM prediction example
 
-model = SAM('sam_b.pt')
-model.info()  # display model information
-model.predict('path/to/image.jpg')  # predict
-```
+!!! example "Segment with prompts"
 
-## Supported Tasks
+    Segment image with given prompts.
+
+    === "Python"
+
+        ```python
+        from ultralytics import SAM
+
+        # Load a model
+        model = SAM('sam_b.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Run inference with bboxes prompt
+        model('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
+
+        # Run inference with points prompt
+        model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
+        ```
+
+!!! example "Segment everything"
+
+    Segment the whole image.
+
+    === "Python"
+
+        ```python
+        from ultralytics import SAM
+
+        # Load a model
+        model = SAM('sam_b.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Run inference
+        model('path/to/image.jpg')
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Run inference with a SAM model
+        yolo predict model=sam_b.pt source=path/to/image.jpg
+        ```
+
+- The logic here is to segment the whole image if you don't pass any prompts(bboxes/points/masks).
+
+!!! example "SAMPredictor example"
+
+    This way you can set image once and run prompts inference multiple times without running image encoder multiple times.
+
+    === "Prompt inference"
+
+        ```python
+        from ultralytics.models.sam import Predictor as SAMPredictor
+
+        # Create SAMPredictor
+        overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model="mobile_sam.pt")
+        predictor = SAMPredictor(overrides=overrides)
+
+        # Set image
+        predictor.set_image("ultralytics/assets/zidane.jpg")  # set with image file
+        predictor.set_image(cv2.imread("ultralytics/assets/zidane.jpg"))  # set with np.ndarray
+        results = predictor(bboxes=[439, 437, 524, 709])
+        results = predictor(points=[900, 370], labels=[1])
+
+        # Reset image
+        predictor.reset_image()
+        ```
+
+    Segment everything with additional args.
+
+    === "Segment everything"
+
+        ```python
+        from ultralytics.models.sam import Predictor as SAMPredictor
+
+        # Create SAMPredictor
+        overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model="mobile_sam.pt")
+        predictor = SAMPredictor(overrides=overrides)
+
+        # Segment with additional args
+        results = predictor(source="ultralytics/assets/zidane.jpg", crop_n_layers=1, points_stride=64)
+        ```
+
+- More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/models/sam/predict.md).
+
+## Available Models and Supported Tasks
 
 | Model Type | Pre-trained Weights | Tasks Supported       |
 |------------|---------------------|-----------------------|
-| sam base   | `sam_b.pt`          | Instance Segmentation |
-| sam large  | `sam_l.pt`          | Instance Segmentation |
+| SAM base   | `sam_b.pt`          | Instance Segmentation |
+| SAM large  | `sam_l.pt`          | Instance Segmentation |
 
-## Supported Modes
+## Operating Modes
 
 | Mode       | Supported          |
 |------------|--------------------|
@@ -48,19 +137,65 @@ model.predict('path/to/image.jpg')  # predict
 | Validation | :x:                |
 | Training   | :x:                |
 
-## Auto-Annotation
+## SAM comparison vs YOLOv8
 
-Auto-annotation is an essential feature that allows you to generate a [segmentation dataset](https://docs.ultralytics.com/datasets/segment) using a pre-trained detection model. It enables you to quickly and accurately annotate a large number of images without the need for manual labeling, saving time and effort.
+Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, [YOLOv8n-seg](../tasks/segment.md):
 
-### Generate Segmentation Dataset Using a Detection Model
+| Model                                          | Size                       | Parameters             | Speed (CPU)                |
+|------------------------------------------------|----------------------------|------------------------|----------------------------|
+| Meta's SAM-b                                   | 358 MB                     | 94.7 M                 | 51096 ms/im                |
+| [MobileSAM](mobile-sam.md)                     | 40.7 MB                    | 10.1 M                 | 46122 ms/im                |
+| [FastSAM-s](fast-sam.md) with YOLOv8 backbone  | 23.7 MB                    | 11.8 M                 | 115 ms/im                  |
+| Ultralytics [YOLOv8n-seg](../tasks/segment.md) | **6.7 MB** (53.4x smaller) | **3.4 M** (27.9x less) | **59 ms/im** (866x faster) |
 
-To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
+This comparison shows the order-of-magnitude differences in the model sizes and speeds between models. Whereas SAM presents unique capabilities for automatic segmenting, it is not a direct competitor to YOLOv8 segment models, which are smaller, faster and more efficient.
 
-```python
-from ultralytics.yolo.data.annotator import auto_annotate
+Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
 
-auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
-```
+
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics import FastSAM, SAM, YOLO
+        
+        # Profile SAM-b
+        model = SAM('sam_b.pt')
+        model.info()
+        model('ultralytics/assets')
+        
+        # Profile MobileSAM
+        model = SAM('mobile_sam.pt')
+        model.info()
+        model('ultralytics/assets')
+        
+        # Profile FastSAM-s
+        model = FastSAM('FastSAM-s.pt')
+        model.info()
+        model('ultralytics/assets')
+        
+        # Profile YOLOv8n-seg
+        model = YOLO('yolov8n-seg.pt')
+        model.info()
+        model('ultralytics/assets')
+        ```
+
+## Auto-Annotation: A Quick Path to Segmentation Datasets
+
+Auto-annotation is a key feature of SAM, allowing users to generate a [segmentation dataset](https://docs.ultralytics.com/datasets/segment) using a pre-trained detection model. This feature enables rapid and accurate annotation of a large number of images, bypassing the need for time-consuming manual labeling.
+
+### Generate Your Segmentation Dataset Using a Detection Model
+
+To auto-annotate your dataset with the Ultralytics framework, use the `auto_annotate` function as shown below:
+
+!!! example ""
+
+    === "Python"
+        ```python
+        from ultralytics.data.annotator import auto_annotate
+        
+        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
+        ```
 
 | Argument   | Type                | Description                                                                                             | Default      |
 |------------|---------------------|---------------------------------------------------------------------------------------------------------|--------------|
@@ -70,23 +205,29 @@ auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt
 | device     | str, optional       | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                    |              |
 | output_dir | str, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | None         |
 
-The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results.
+The `auto_annotate` function takes the path to your images, with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results.
 
-By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
+Auto-annotation with pre-trained models can dramatically cut down the time and effort required for creating high-quality segmentation datasets. This feature is especially beneficial for researchers and developers dealing with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
 
 ## Citations and Acknowledgements
 
-If you use SAM in your research or development work, please cite the following paper:
+If you find SAM useful in your research or development work, please consider citing our paper:
 
-```bibtex
-@misc{kirillov2023segment,
-      title={Segment Anything}, 
-      author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
-      year={2023},
-      eprint={2304.02643},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
 
-We would like to acknowledge Meta AI for creating and maintaining this valuable resource for the computer vision community.
+    === "BibTeX"
+
+        ```bibtex
+        @misc{kirillov2023segment,
+              title={Segment Anything},
+              author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
+              year={2023},
+              eprint={2304.02643},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.
+
+*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*

downloads/ultralytics-main/docs/models/yolo-nas.md

@@ -0,0 +1,127 @@
+---
+comments: true
+description: Explore detailed documentation of YOLO-NAS, a superior object detection model. Learn about its features, pre-trained models, usage with Ultralytics Python API, and more.
+keywords: YOLO-NAS, Deci AI, object detection, deep learning, neural architecture search, Ultralytics Python API, YOLO model, pre-trained models, quantization, optimization, COCO, Objects365, Roboflow 100
+---
+
+# YOLO-NAS
+
+## Overview
+
+Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational model. It is the product of advanced Neural Architecture Search technology, meticulously designed to address the limitations of previous YOLO models. With significant improvements in quantization support and accuracy-latency trade-offs, YOLO-NAS represents a major leap in object detection.
+
+![Model example image](https://learnopencv.com/wp-content/uploads/2023/05/yolo-nas_COCO_map_metrics.png)
+**Overview of YOLO-NAS.** YOLO-NAS employs quantization-aware blocks and selective quantization for optimal performance. The model, when converted to its INT8 quantized version, experiences a minimal precision drop, a significant improvement over other models. These advancements culminate in a superior architecture with unprecedented object detection capabilities and outstanding performance.
+
+### Key Features
+
+- **Quantization-Friendly Basic Block:** YOLO-NAS introduces a new basic block that is friendly to quantization, addressing one of the significant limitations of previous YOLO models.
+- **Sophisticated Training and Quantization:** YOLO-NAS leverages advanced training schemes and post-training quantization to enhance performance.
+- **AutoNAC Optimization and Pre-training:** YOLO-NAS utilizes AutoNAC optimization and is pre-trained on prominent datasets such as COCO, Objects365, and Roboflow 100. This pre-training makes it extremely suitable for downstream object detection tasks in production environments.
+
+## Pre-trained Models
+
+Experience the power of next-generation object detection with the pre-trained YOLO-NAS models provided by Ultralytics. These models are designed to deliver top-notch performance in terms of both speed and accuracy. Choose from a variety of options tailored to your specific needs:
+
+| Model            | mAP   | Latency (ms) |
+|------------------|-------|--------------|
+| YOLO-NAS S       | 47.5  | 3.21         |
+| YOLO-NAS M       | 51.55 | 5.85         |
+| YOLO-NAS L       | 52.22 | 7.87         |
+| YOLO-NAS S INT-8 | 47.03 | 2.36         |
+| YOLO-NAS M INT-8 | 51.0  | 3.78         |
+| YOLO-NAS L INT-8 | 52.1  | 4.78         |
+
+Each model variant is designed to offer a balance between Mean Average Precision (mAP) and latency, helping you optimize your object detection tasks for both performance and speed.
+
+## Usage
+
+Ultralytics has made YOLO-NAS models easy to integrate into your Python applications via our `ultralytics` python package. The package provides a user-friendly Python API to streamline the process.
+
+The following examples show how to use YOLO-NAS models with the `ultralytics` package for inference and validation:
+
+### Inference and Validation Examples
+
+In this example we validate YOLO-NAS-s on the COCO8 dataset.
+
+!!! example ""
+
+    This example provides simple inference and validation code for YOLO-NAS. For handling inference results see [Predict](../modes/predict.md) mode. For using YOLO-NAS with additional modes see [Val](../modes/val.md) and [Export](../modes/export.md). YOLO-NAS on the `ultralytics` package does not support training.
+
+    === "Python"
+
+        PyTorch pretrained `*.pt` models files can be passed to the `NAS()` class to create a model instance in python:
+
+        ```python
+        from ultralytics import NAS
+
+        # Load a COCO-pretrained YOLO-NAS-s model
+        model = NAS('yolo_nas_s.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Validate the model on the COCO8 example dataset
+        results = model.val(data='coco8.yaml')
+
+        # Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        CLI commands are available to directly run the models:
+
+        ```bash
+        # Load a COCO-pretrained YOLO-NAS-s model and validate it's performance on the COCO8 example dataset
+        yolo val model=yolo_nas_s.pt data=coco8.yaml
+
+        # Load a COCO-pretrained YOLO-NAS-s model and run inference on the 'bus.jpg' image
+        yolo predict model=yolo_nas_s.pt source=path/to/bus.jpg
+        ```
+
+### Supported Tasks
+
+The YOLO-NAS models are primarily designed for object detection tasks. You can download the pre-trained weights for each variant of the model as follows:
+
+| Model Type | Pre-trained Weights                                                                           | Tasks Supported  |
+|------------|-----------------------------------------------------------------------------------------------|------------------|
+| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_s.pt) | Object Detection |
+| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_m.pt) | Object Detection |
+| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo_nas_l.pt) | Object Detection |
+
+### Supported Modes
+
+The YOLO-NAS models support both inference and validation modes, allowing you to predict and validate results with ease. Training mode, however, is currently not supported.
+
+| Mode       | Supported          |
+|------------|--------------------|
+| Inference  | :heavy_check_mark: |
+| Validation | :heavy_check_mark: |
+| Training   | :x:                |
+
+Harness the power of the YOLO-NAS models to drive your object detection tasks to new heights of performance and speed.
+
+## Citations and Acknowledgements
+
+If you employ YOLO-NAS in your research or development work, please cite SuperGradients:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{supergradients,
+              doi = {10.5281/ZENODO.7789328},
+              url = {https://zenodo.org/record/7789328},
+              author = {Aharon,  Shay and {Louis-Dupont} and {Ofri Masad} and Yurkova,  Kate and {Lotem Fridman} and {Lkdci} and Khvedchenya,  Eugene and Rubin,  Ran and Bagrov,  Natan and Tymchenko,  Borys and Keren,  Tomer and Zhilko,  Alexander and {Eran-Deci}},
+              title = {Super-Gradients},
+              publisher = {GitHub},
+              journal = {GitHub repository},
+              year = {2021},
+        }
+        ```
+
+We express our gratitude to Deci AI's [SuperGradients](https://github.com/Deci-AI/super-gradients/) team for their efforts in creating and maintaining this valuable resource for the computer vision community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.
+
+*Keywords: YOLO-NAS, Deci AI, object detection, deep learning, neural architecture search, Ultralytics Python API, YOLO model, SuperGradients, pre-trained models, quantization-friendly basic block, advanced training schemes, post-training quantization, AutoNAC optimization, COCO, Objects365, Roboflow 100*

downloads/ultralytics-main/docs/models/yolov3.md

@@ -1,7 +1,107 @@
 ---
 comments: true
+description: Get an overview of YOLOv3, YOLOv3-Ultralytics and YOLOv3u. Learn about their key features, usage, and supported tasks for object detection.
+keywords: YOLOv3, YOLOv3-Ultralytics, YOLOv3u, Object Detection, Inference, Training, Ultralytics
 ---
 
-# 🚧Page Under Construction ⚒
+# YOLOv3, YOLOv3-Ultralytics, and YOLOv3u
 
-This page is currently under construction!️👷Please check back later for updates. 😃🔜
+## Overview
+
+This document presents an overview of three closely related object detection models, namely [YOLOv3](https://pjreddie.com/darknet/yolo/), [YOLOv3-Ultralytics](https://github.com/ultralytics/yolov3), and [YOLOv3u](https://github.com/ultralytics/ultralytics).
+
+1. **YOLOv3:** This is the third version of the You Only Look Once (YOLO) object detection algorithm. Originally developed by Joseph Redmon, YOLOv3 improved on its predecessors by introducing features such as multiscale predictions and three different sizes of detection kernels.
+
+2. **YOLOv3-Ultralytics:** This is Ultralytics' implementation of the YOLOv3 model. It reproduces the original YOLOv3 architecture and offers additional functionalities, such as support for more pre-trained models and easier customization options.
+
+3. **YOLOv3u:** This is an updated version of YOLOv3-Ultralytics that incorporates the anchor-free, objectness-free split head used in YOLOv8 models. YOLOv3u maintains the same backbone and neck architecture as YOLOv3 but with the updated detection head from YOLOv8.
+
+![Ultralytics YOLOv3](https://raw.githubusercontent.com/ultralytics/assets/main/yolov3/banner-yolov3.png)
+
+## Key Features
+
+- **YOLOv3:** Introduced the use of three different scales for detection, leveraging three different sizes of detection kernels: 13x13, 26x26, and 52x52. This significantly improved detection accuracy for objects of different sizes. Additionally, YOLOv3 added features such as multi-label predictions for each bounding box and a better feature extractor network.
+
+- **YOLOv3-Ultralytics:** Ultralytics' implementation of YOLOv3 provides the same performance as the original model but comes with added support for more pre-trained models, additional training methods, and easier customization options. This makes it more versatile and user-friendly for practical applications.
+
+- **YOLOv3u:** This updated model incorporates the anchor-free, objectness-free split head from YOLOv8. By eliminating the need for pre-defined anchor boxes and objectness scores, this detection head design can improve the model's ability to detect objects of varying sizes and shapes. This makes YOLOv3u more robust and accurate for object detection tasks.
+
+## Supported Tasks
+
+YOLOv3, YOLOv3-Ultralytics, and YOLOv3u all support the following tasks:
+
+- Object Detection
+
+## Supported Modes
+
+All three models support the following modes:
+
+- Inference
+- Validation
+- Training
+- Export
+
+## Performance
+
+Below is a comparison of the performance of the three models. The performance is measured in terms of the Mean Average Precision (mAP) on the COCO dataset:
+
+TODO
+
+## Usage
+
+You can use YOLOv3 for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv3 model for inference:
+
+!!! example ""
+
+    This example provides simple inference code for YOLOv3. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv3 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+
+    === "Python"
+
+        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a COCO-pretrained YOLOv3n model
+        model = YOLO('yolov3n.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the YOLOv3n model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        CLI commands are available to directly run the models:
+
+        ```bash
+        # Load a COCO-pretrained YOLOv3n model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov3n.pt data=coco8.yaml epochs=100 imgsz=640
+
+        # Load a COCO-pretrained YOLOv3n model and run inference on the 'bus.jpg' image
+        yolo predict model=yolov3n.pt source=path/to/bus.jpg
+        ```
+
+## Citations and Acknowledgements
+
+If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{redmon2018yolov3,
+          title={YOLOv3: An Incremental Improvement},
+          author={Redmon, Joseph and Farhadi, Ali},
+          journal={arXiv preprint arXiv:1804.02767},
+          year={2018}
+        }
+        ```
+
+Thank you to Joseph Redmon and Ali Farhadi for developing the original YOLOv3.

downloads/ultralytics-main/docs/models/yolov4.md

@@ -0,0 +1,71 @@
+---
+comments: true
+description: Explore our detailed guide on YOLOv4, a state-of-the-art real-time object detector. Understand its architectural highlights, innovative features, and application examples.
+keywords: ultralytics, YOLOv4, object detection, neural network, real-time detection, object detector, machine learning
+---
+
+# YOLOv4: High-Speed and Precise Object Detection
+
+Welcome to the Ultralytics documentation page for YOLOv4, a state-of-the-art, real-time object detector launched in 2020 by Alexey Bochkovskiy at [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet). YOLOv4 is designed to provide the optimal balance between speed and accuracy, making it an excellent choice for many applications.
+
+![YOLOv4 architecture diagram](https://user-images.githubusercontent.com/26833433/246185689-530b7fe8-737b-4bb0-b5dd-de10ef5aface.png)
+**YOLOv4 architecture diagram**. Showcasing the intricate network design of YOLOv4, including the backbone, neck, and head components, and their interconnected layers for optimal real-time object detection.
+
+## Introduction
+
+YOLOv4 stands for You Only Look Once version 4. It is a real-time object detection model developed to address the limitations of previous YOLO versions like [YOLOv3](./yolov3.md) and other object detection models. Unlike other convolutional neural network (CNN) based object detectors, YOLOv4 is not only applicable for recommendation systems but also for standalone process management and human input reduction. Its operation on conventional graphics processing units (GPUs) allows for mass usage at an affordable price, and it is designed to work in real-time on a conventional GPU while requiring only one such GPU for training.
+
+## Architecture
+
+YOLOv4 makes use of several innovative features that work together to optimize its performance. These include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), Mish-activation, Mosaic data augmentation, DropBlock regularization, and CIoU loss. These features are combined to achieve state-of-the-art results.
+
+A typical object detector is composed of several parts including the input, the backbone, the neck, and the head. The backbone of YOLOv4 is pre-trained on ImageNet and is used to predict classes and bounding boxes of objects. The backbone could be from several models including VGG, ResNet, ResNeXt, or DenseNet. The neck part of the detector is used to collect feature maps from different stages and usually includes several bottom-up paths and several top-down paths. The head part is what is used to make the final object detections and classifications.
+
+## Bag of Freebies
+
+YOLOv4 also makes use of methods known as "bag of freebies," which are techniques that improve the accuracy of the model during training without increasing the cost of inference. Data augmentation is a common bag of freebies technique used in object detection, which increases the variability of the input images to improve the robustness of the model. Some examples of data augmentation include photometric distortions (adjusting the brightness, contrast, hue, saturation, and noise of an image) and geometric distortions (adding random scaling, cropping, flipping, and rotating). These techniques help the model to generalize better to different types of images.
+
+## Features and Performance
+
+YOLOv4 is designed for optimal speed and accuracy in object detection. The architecture of YOLOv4 includes CSPDarknet53 as the backbone, PANet as the neck, and YOLOv3 as the detection head. This design allows YOLOv4 to perform object detection at an impressive speed, making it suitable for real-time applications. YOLOv4 also excels in accuracy, achieving state-of-the-art results in object detection benchmarks.
+
+## Usage Examples
+
+As of the time of writing, Ultralytics does not currently support YOLOv4 models. Therefore, any users interested in using YOLOv4 will need to refer directly to the YOLOv4 GitHub repository for installation and usage instructions.
+
+Here is a brief overview of the typical steps you might take to use YOLOv4:
+
+1. Visit the YOLOv4 GitHub repository: [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet).
+
+2. Follow the instructions provided in the README file for installation. This typically involves cloning the repository, installing necessary dependencies, and setting up any necessary environment variables.
+
+3. Once installation is complete, you can train and use the model as per the usage instructions provided in the repository. This usually involves preparing your dataset, configuring the model parameters, training the model, and then using the trained model to perform object detection.
+
+Please note that the specific steps may vary depending on your specific use case and the current state of the YOLOv4 repository. Therefore, it is strongly recommended to refer directly to the instructions provided in the YOLOv4 GitHub repository.
+
+We regret any inconvenience this may cause and will strive to update this document with usage examples for Ultralytics once support for YOLOv4 is implemented.
+
+## Conclusion
+
+YOLOv4 is a powerful and efficient object detection model that strikes a balance between speed and accuracy. Its use of unique features and bag of freebies techniques during training allows it to perform excellently in real-time object detection tasks. YOLOv4 can be trained and used by anyone with a conventional GPU, making it accessible and practical for a wide range of applications.
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the YOLOv4 authors for their significant contributions in the field of real-time object detection:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{bochkovskiy2020yolov4,
+              title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
+              author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
+              year={2020},
+              eprint={2004.10934},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/pdf/2004.10934.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/AlexeyAB/darknet). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

downloads/ultralytics-main/docs/models/yolov5.md

@@ -1,19 +1,24 @@
 ---
 comments: true
-description: Detect objects faster and more accurately using Ultralytics YOLOv5u. Find pre-trained models for each task, including Inference, Validation and Training.
+description: Discover YOLOv5u, a boosted version of the YOLOv5 model featuring an improved accuracy-speed tradeoff and numerous pre-trained models for various object detection tasks.
+keywords: YOLOv5u, object detection, pre-trained models, Ultralytics, Inference, Validation, YOLOv5, YOLOv8, anchor-free, objectness-free, real-time applications, machine learning
 ---
 
-# YOLOv5u
+# YOLOv5
 
 ## Overview
 
-YOLOv5u is an updated version of YOLOv5 that incorporates the anchor-free split Ultralytics head used in the YOLOv8 models. It retains the same backbone and neck architecture as YOLOv5 but offers improved accuracy-speed tradeoff for object detection tasks.
+YOLOv5u represents an advancement in object detection methodologies. Originating from the foundational architecture of the [YOLOv5](https://github.com/ultralytics/yolov5) model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the [YOLOv8](./yolov8.md) models. This adaptation refines the model's architecture, leading to an improved accuracy-speed tradeoff in object detection tasks. Given the empirical results and its derived features, YOLOv5u provides an efficient alternative for those seeking robust solutions in both research and practical applications.
+
+![Ultralytics YOLOv5](https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png)
 
 ## Key Features
 
-- **Anchor-free Split Ultralytics Head:** YOLOv5u replaces the traditional anchor-based detection head with an anchor-free split Ultralytics head, resulting in improved performance.
-- **Optimized Accuracy-Speed Tradeoff:** The updated model offers a better balance between accuracy and speed, making it more suitable for a wider range of applications.
-- **Variety of Pre-trained Models:** YOLOv5u offers a range of pre-trained models tailored for various tasks, including Inference, Validation, and Training.
+- **Anchor-free Split Ultralytics Head:** Traditional object detection models rely on predefined anchor boxes to predict object locations. However, YOLOv5u modernizes this approach. By adopting an anchor-free split Ultralytics head, it ensures a more flexible and adaptive detection mechanism, consequently enhancing the performance in diverse scenarios.
+
+- **Optimized Accuracy-Speed Tradeoff:** Speed and accuracy often pull in opposite directions. But YOLOv5u challenges this tradeoff. It offers a calibrated balance, ensuring real-time detections without compromising on accuracy. This feature is particularly invaluable for applications that demand swift responses, such as autonomous vehicles, robotics, and real-time video analytics.
+
+- **Variety of Pre-trained Models:** Understanding that different tasks require different toolsets, YOLOv5u provides a plethora of pre-trained models. Whether you're focusing on Inference, Validation, or Training, there's a tailor-made model awaiting you. This variety ensures you're not just using a one-size-fits-all solution, but a model specifically fine-tuned for your unique challenge.
 
 ## Supported Tasks
 
@@ -29,20 +34,82 @@ YOLOv5u is an updated version of YOLOv5 that incorporates the anchor-free split
 | Validation | :heavy_check_mark: |
 | Training   | :heavy_check_mark: |
 
-??? Performance
+!!! Performance
 
     === "Detection"
 
-        | Model                                                                                    | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
-        | ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
-        | [YOLOv5nu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5nu.pt)   | 640                   | 34.3                 | 73.6                           | 1.06                                | 2.6                | 7.7               |
-        | [YOLOv5su](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5su.pt)   | 640                   | 43.0                 | 120.7                          | 1.27                                | 9.1                | 24.0              |
-        | [YOLOv5mu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5mu.pt)   | 640                   | 49.0                 | 233.9                          | 1.86                                | 25.1               | 64.2              |
-        | [YOLOv5lu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5lu.pt)   | 640                   | 52.2                 | 408.4                          | 2.50                                | 53.2               | 135.0             |
-        | [YOLOv5xu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5xu.pt)   | 640                   | 53.2                 | 763.2                          | 3.81                                | 97.2               | 246.4             |
-        |                                                                                          |                       |                      |                                |                                     |                    |                   |
-        | [YOLOv5n6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5n6u.pt) | 1280                  | 42.1                 | -                              | -                                   | 4.3                | 7.8               |
-        | [YOLOv5s6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | 1280                  | 48.6                 | -                              | -                                   | 15.3               | 24.6              |
-        | [YOLOv5m6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | 1280                  | 53.6                 | -                              | -                                   | 41.2               | 65.7              |
-        | [YOLOv5l6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | 1280                  | 55.7                 | -                              | -                                   | 86.1               | 137.4             |
-        | [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 1280                  | 56.8                 | -                              | -                                   | 155.4              | 250.7             |
+    | Model                                                                                       | YAML                                                                                                           | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+    |---------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
+    | [yolov5nu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5nu.pt)   | [yolov5n.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 34.3                 | 73.6                           | 1.06                                | 2.6                | 7.7               |
+    | [yolov5su.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5su.pt)   | [yolov5s.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 43.0                 | 120.7                          | 1.27                                | 9.1                | 24.0              |
+    | [yolov5mu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5mu.pt)   | [yolov5m.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 49.0                 | 233.9                          | 1.86                                | 25.1               | 64.2              |
+    | [yolov5lu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5lu.pt)   | [yolov5l.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 52.2                 | 408.4                          | 2.50                                | 53.2               | 135.0             |
+    | [yolov5xu.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5xu.pt)   | [yolov5x.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5.yaml)     | 640                   | 53.2                 | 763.2                          | 3.81                                | 97.2               | 246.4             |
+    |                                                                                             |                                                                                                                |                       |                      |                                |                                     |                    |                   |
+    | [yolov5n6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5n6u.pt) | [yolov5n6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 42.1                 | 211.0                          | 1.83                                | 4.3                | 7.8               |
+    | [yolov5s6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | [yolov5s6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 48.6                 | 422.6                          | 2.34                                | 15.3               | 24.6              |
+    | [yolov5m6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | [yolov5m6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 53.6                 | 810.9                          | 4.36                                | 41.2               | 65.7              |
+    | [yolov5l6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | [yolov5l6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 55.7                 | 1470.9                         | 5.47                                | 86.1               | 137.4             |
+    | [yolov5x6u.pt](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | [yolov5x6.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/v5/yolov5-p6.yaml) | 1280                  | 56.8                 | 2436.5                         | 8.98                                | 155.4              | 250.7             |
+
+## Usage
+
+You can use YOLOv5u for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv5u model for inference:
+
+!!! example ""
+
+    This example provides simple inference code for YOLOv5. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv5 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+
+    === "Python"
+
+        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a COCO-pretrained YOLOv5n model
+        model = YOLO('yolov5n.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the YOLOv5n model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        CLI commands are available to directly run the models:
+
+        ```bash
+        # Load a COCO-pretrained YOLOv5n model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov5n.pt data=coco8.yaml epochs=100 imgsz=640
+
+        # Load a COCO-pretrained YOLOv5n model and run inference on the 'bus.jpg' image
+        yolo predict model=yolov5n.pt source=path/to/bus.jpg
+        ```
+
+## Citations and Acknowledgements
+
+If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows:
+
+!!! note ""
+
+    === "BibTeX"
+        ```bibtex
+        @software{yolov5,
+          title = {Ultralytics YOLOv5},
+          author = {Glenn Jocher},
+          year = {2020},
+          version = {7.0},
+          license = {AGPL-3.0},
+          url = {https://github.com/ultralytics/yolov5},
+          doi = {10.5281/zenodo.3908559},
+          orcid = {0000-0001-5950-6979}
+        }
+        ```
+
+Special thanks to Glenn Jocher and the Ultralytics team for their work on developing and maintaining the YOLOv5 and YOLOv5u models.

downloads/ultralytics-main/docs/models/yolov6.md

@@ -0,0 +1,114 @@
+---
+comments: true
+description: Explore Meituan YOLOv6, a state-of-the-art object detection model striking a balance between speed and accuracy. Dive into features, pre-trained models, and Python usage.
+keywords: Meituan YOLOv6, object detection, Ultralytics, YOLOv6 docs, Bi-directional Concatenation, Anchor-Aided Training, pretrained models, real-time applications
+---
+
+# Meituan YOLOv6
+
+## Overview
+
+[Meituan](https://about.meituan.com/) YOLOv6 is a cutting-edge object detector that offers remarkable balance between speed and accuracy, making it a popular choice for real-time applications. This model introduces several notable enhancements on its architecture and training scheme, including the implementation of a Bi-directional Concatenation (BiC) module, an anchor-aided training (AAT) strategy, and an improved backbone and neck design for state-of-the-art accuracy on the COCO dataset.
+
+![Meituan YOLOv6](https://user-images.githubusercontent.com/26833433/240750495-4da954ce-8b3b-41c4-8afd-ddb74361d3c2.png)
+![Model example image](https://user-images.githubusercontent.com/26833433/240750557-3e9ec4f0-0598-49a8-83ea-f33c91eb6d68.png)
+**Overview of YOLOv6.** Model architecture diagram showing the redesigned network components and training strategies that have led to significant performance improvements. (a) The neck of YOLOv6 (N and S are shown). Note for M/L, RepBlocks is replaced with CSPStackRep. (b) The
+structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/pdf/2301.05586.pdf)).
+
+### Key Features
+
+- **Bidirectional Concatenation (BiC) Module:** YOLOv6 introduces a BiC module in the neck of the detector, enhancing localization signals and delivering performance gains with negligible speed degradation.
+- **Anchor-Aided Training (AAT) Strategy:** This model proposes AAT to enjoy the benefits of both anchor-based and anchor-free paradigms without compromising inference efficiency.
+- **Enhanced Backbone and Neck Design:** By deepening YOLOv6 to include another stage in the backbone and neck, this model achieves state-of-the-art performance on the COCO dataset at high-resolution input.
+- **Self-Distillation Strategy:** A new self-distillation strategy is implemented to boost the performance of smaller models of YOLOv6, enhancing the auxiliary regression branch during training and removing it at inference to avoid a marked speed decline.
+
+## Pre-trained Models
+
+YOLOv6 provides various pre-trained models with different scales:
+
+- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA Tesla T4 GPU.
+- YOLOv6-S: 45.0% AP at 484 FPS.
+- YOLOv6-M: 50.0% AP at 226 FPS.
+- YOLOv6-L: 52.8% AP at 116 FPS.
+- YOLOv6-L6: State-of-the-art accuracy in real-time.
+
+YOLOv6 also provides quantized models for different precisions and models optimized for mobile platforms.
+
+## Usage
+
+You can use YOLOv6 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv6 models for training:
+
+!!! example ""
+
+    This example provides simple training code for YOLOv6. For more options including training settings see [Train](../modes/train.md) mode. For using YOLOv6 with additional modes see [Predict](../modes/predict.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+
+    === "Python"
+
+        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
+
+        ```python
+        from ultralytics import YOLO
+
+        # Build a YOLOv6n model from scratch
+        model = YOLO('yolov6n.yaml')
+
+        # Display model information (optional)
+        model.info()
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the YOLOv6n model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        CLI commands are available to directly run the models:
+
+        ```bash
+        # Build a YOLOv6n model from scratch and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov6n.yaml data=coco8.yaml epochs=100 imgsz=640
+
+        # Build a YOLOv6n model from scratch and run inference on the 'bus.jpg' image
+        yolo predict model=yolov6n.yaml source=path/to/bus.jpg
+        ```
+
+### Supported Tasks
+
+| Model Type | Pre-trained Weights | Tasks Supported  |
+|------------|---------------------|------------------|
+| YOLOv6-N   | `yolov6-n.pt`       | Object Detection |
+| YOLOv6-S   | `yolov6-s.pt`       | Object Detection |
+| YOLOv6-M   | `yolov6-m.pt`       | Object Detection |
+| YOLOv6-L   | `yolov6-l.pt`       | Object Detection |
+| YOLOv6-L6  | `yolov6-l6.pt`      | Object Detection |
+
+## Supported Modes
+
+| Mode       | Supported          |
+|------------|--------------------|
+| Inference  | :heavy_check_mark: |
+| Validation | :heavy_check_mark: |
+| Training   | :heavy_check_mark: |
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{li2023yolov6,
+              title={YOLOv6 v3.0: A Full-Scale Reloading},
+              author={Chuyi Li and Lulu Li and Yifei Geng and Hongliang Jiang and Meng Cheng and Bo Zhang and Zaidan Ke and Xiaoming Xu and Xiangxiang Chu},
+              year={2023},
+              eprint={2301.05586},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

downloads/ultralytics-main/docs/models/yolov7.md

@@ -0,0 +1,65 @@
+---
+comments: true
+description: Explore the YOLOv7, a real-time object detector. Understand its superior speed, impressive accuracy, and unique trainable bag-of-freebies optimization focus.
+keywords: YOLOv7, real-time object detector, state-of-the-art, Ultralytics, MS COCO dataset, model re-parameterization, dynamic label assignment, extended scaling, compound scaling
+---
+
+# YOLOv7: Trainable Bag-of-Freebies
+
+YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS. It has the highest accuracy (56.8% AP) among all known real-time object detectors with 30 FPS or higher on GPU V100. Moreover, YOLOv7 outperforms other object detectors such as YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, and many others in speed and accuracy. The model is trained on the MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code for YOLOv7 is available on GitHub.
+
+![YOLOv7 comparison with SOTA object detectors](https://github.com/ultralytics/ultralytics/assets/26833433/5e1e0420-8122-4c79-b8d0-2860aa79af92)
+**Comparison of state-of-the-art object detectors.** From the results in Table 2 we know that the proposed method has the best speed-accuracy trade-off comprehensively. If we compare YOLOv7-tiny-SiLU with YOLOv5-N (r6.1), our method is 127 fps faster and 10.7% more accurate on AP. In addition, YOLOv7 has 51.4% AP at frame rate of 161 fps, while PPYOLOE-L with the same AP has only 78 fps frame rate. In terms of parameter usage, YOLOv7 is 41% less than PPYOLOE-L. If we compare YOLOv7-X with 114 fps inference speed to YOLOv5-L (r6.1) with 99 fps inference speed, YOLOv7-X can improve AP by 3.9%. If YOLOv7-X is compared with YOLOv5-X (r6.1) of similar scale, the inference speed of YOLOv7-X is 31 fps faster. In addition, in terms the amount of parameters and computation, YOLOv7-X reduces 22% of parameters and 8% of computation compared to YOLOv5-X (r6.1), but improves AP by 2.2% ([Source](https://arxiv.org/pdf/2207.02696.pdf)).
+
+## Overview
+
+Real-time object detection is an important component in many computer vision systems, including multi-object tracking, autonomous driving, robotics, and medical image analysis. In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
+
+Unlike traditional real-time object detectors that focus on architecture optimization, YOLOv7 introduces a focus on the optimization of the training process. This includes modules and optimization methods designed to improve the accuracy of object detection without increasing the inference cost, a concept known as the "trainable bag-of-freebies".
+
+## Key Features
+
+YOLOv7 introduces several key features:
+
+1. **Model Re-parameterization**: YOLOv7 proposes a planned re-parameterized model, which is a strategy applicable to layers in different networks with the concept of gradient propagation path.
+
+2. **Dynamic Label Assignment**: The training of the model with multiple output layers presents a new issue: "How to assign dynamic targets for the outputs of different branches?" To solve this problem, YOLOv7 introduces a new label assignment method called coarse-to-fine lead guided label assignment.
+
+3. **Extended and Compound Scaling**: YOLOv7 proposes "extend" and "compound scaling" methods for the real-time object detector that can effectively utilize parameters and computation.
+
+4. **Efficiency**: The method proposed by YOLOv7 can effectively reduce about 40% parameters and 50% computation of state-of-the-art real-time object detector, and has faster inference speed and higher detection accuracy.
+
+## Usage Examples
+
+As of the time of writing, Ultralytics does not currently support YOLOv7 models. Therefore, any users interested in using YOLOv7 will need to refer directly to the YOLOv7 GitHub repository for installation and usage instructions.
+
+Here is a brief overview of the typical steps you might take to use YOLOv7:
+
+1. Visit the YOLOv7 GitHub repository: [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7).
+
+2. Follow the instructions provided in the README file for installation. This typically involves cloning the repository, installing necessary dependencies, and setting up any necessary environment variables.
+
+3. Once installation is complete, you can train and use the model as per the usage instructions provided in the repository. This usually involves preparing your dataset, configuring the model parameters, training the model, and then using the trained model to perform object detection.
+
+Please note that the specific steps may vary depending on your specific use case and the current state of the YOLOv7 repository. Therefore, it is strongly recommended to refer directly to the instructions provided in the YOLOv7 GitHub repository.
+
+We regret any inconvenience this may cause and will strive to update this document with usage examples for Ultralytics once support for YOLOv7 is implemented.
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the YOLOv7 authors for their significant contributions in the field of real-time object detection:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{wang2022yolov7,
+          title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
+          author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
+          journal={arXiv preprint arXiv:2207.02696},
+          year={2022}
+        }
+        ```
+
+The original YOLOv7 paper can be found on [arXiv](https://arxiv.org/pdf/2207.02696.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/WongKinYiu/yolov7). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

downloads/ultralytics-main/docs/models/yolov8.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Learn about YOLOv8's pre-trained weights supporting detection, instance segmentation, pose, and classification tasks. Get performance details.
+description: Explore the thrilling features of YOLOv8, the latest version of our real-time object detector! Learn how advanced architectures, pre-trained models and optimal balance between accuracy & speed make YOLOv8 the perfect choice for your object detection tasks.
+keywords: YOLOv8, Ultralytics, real-time object detector, pre-trained models, documentation, object detection, YOLO series, advanced architectures, accuracy, speed
 ---
 
 # YOLOv8
@@ -9,6 +10,8 @@ description: Learn about YOLOv8's pre-trained weights supporting detection, inst
 
 YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various object detection tasks in a wide range of applications.
 
+![Ultralytics YOLOv8](https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png)
+
 ## Key Features
 
 - **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved feature extraction and object detection performance.
@@ -18,12 +21,12 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
 
 ## Supported Tasks
 
-| Model Type  | Pre-trained Weights                                                                                              | Task                  |
-|-------------|------------------------------------------------------------------------------------------------------------------|-----------------------|
-| YOLOv8      | `yolov8n.pt`, `yolov8s.pt`, `yolov8m.pt`, `yolov8l.pt`, `yolov8x.pt`                                             | Detection             |
-| YOLOv8-seg  | `yolov8n-seg.pt`, `yolov8s-seg.pt`, `yolov8m-seg.pt`, `yolov8l-seg.pt`, `yolov8x-seg.pt`                         | Instance Segmentation |
-| YOLOv8-pose | `yolov8n-pose.pt`, `yolov8s-pose.pt`, `yolov8m-pose.pt`, `yolov8l-pose.pt`, `yolov8x-pose.pt` ,`yolov8x-pose-p6` | Pose/Keypoints        |
-| YOLOv8-cls  | `yolov8n-cls.pt`, `yolov8s-cls.pt`, `yolov8m-cls.pt`, `yolov8l-cls.pt`, `yolov8x-cls.pt`                         | Classification        |
+| Model Type  | Pre-trained Weights                                                                                                 | Task                  |
+|-------------|---------------------------------------------------------------------------------------------------------------------|-----------------------|
+| YOLOv8      | `yolov8n.pt`, `yolov8s.pt`, `yolov8m.pt`, `yolov8l.pt`, `yolov8x.pt`                                                | Detection             |
+| YOLOv8-seg  | `yolov8n-seg.pt`, `yolov8s-seg.pt`, `yolov8m-seg.pt`, `yolov8l-seg.pt`, `yolov8x-seg.pt`                            | Instance Segmentation |
+| YOLOv8-pose | `yolov8n-pose.pt`, `yolov8s-pose.pt`, `yolov8m-pose.pt`, `yolov8l-pose.pt`, `yolov8x-pose.pt`, `yolov8x-pose-p6.pt` | Pose/Keypoints        |
+| YOLOv8-cls  | `yolov8n-cls.pt`, `yolov8s-cls.pt`, `yolov8m-cls.pt`, `yolov8l-cls.pt`, `yolov8x-cls.pt`                            | Classification        |
 
 ## Supported Modes
 
@@ -33,7 +36,7 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
 | Validation | :heavy_check_mark: |
 | Training   | :heavy_check_mark: |
 
-??? Performance
+!!! Performance
 
     === "Detection"
 
@@ -74,4 +77,66 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
         | [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |
         | [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |
         | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
-        | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |
+        | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |
+
+## Usage
+
+You can use YOLOv8 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv8 models for inference:
+
+!!! example ""
+
+    This example provides simple inference code for YOLOv8. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv8 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+
+    === "Python"
+
+        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a COCO-pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Display model information (optional)
+        model.info()
+
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+
+        # Run inference with the YOLOv8n model on the 'bus.jpg' image
+        results = model('path/to/bus.jpg')
+        ```
+
+    === "CLI"
+
+        CLI commands are available to directly run the models:
+
+        ```bash
+        # Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640
+
+        # Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image
+        yolo predict model=yolov8n.pt source=path/to/bus.jpg
+        ```
+
+## Citations and Acknowledgements
+
+If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @software{yolov8_ultralytics,
+          author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
+          title = {Ultralytics YOLOv8},
+          version = {8.0.0},
+          year = {2023},
+          url = {https://github.com/ultralytics/ultralytics},
+          orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
+          license = {AGPL-3.0}
+        }
+        ```
+
+Please note that the DOI is pending and will be added to the citation once it is available. The usage of the software is in accordance with the AGPL-3.0 license.

downloads/ultralytics-main/docs/modes/benchmark.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Benchmark mode compares speed and accuracy of various YOLOv8 export formats like ONNX or OpenVINO. Optimize formats for speed or accuracy.
+description: Learn how to profile speed and accuracy of YOLOv8 across various export formats; get insights on mAP50-95, accuracy_top5 metrics, and more.
+keywords: Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling, mAP50-95, accuracy_top5, ONNX, OpenVINO, TensorRT, YOLO export formats
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@@ -24,50 +25,52 @@ full list of export arguments.
 !!! example ""
 
     === "Python"
-    
+
         ```python
-        from ultralytics.yolo.utils.benchmarks import benchmark
-        
+        from ultralytics.utils.benchmarks import benchmark
+
         # Benchmark on GPU
-        benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0)
+        benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
         ```
     === "CLI"
-    
+
         ```bash
-        yolo benchmark model=yolov8n.pt imgsz=640 half=False device=0
+        yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
         ```
 
 ## Arguments
 
-Arguments such as `model`, `imgsz`, `half`, `device`, and `hard_fail` provide users with the flexibility to fine-tune
+Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` provide users with the flexibility to fine-tune
 the benchmarks to their specific needs and compare the performance of different export formats with ease.
 
-| Key         | Value   | Description                                                          |
-|-------------|---------|----------------------------------------------------------------------|
-| `model`     | `None`  | path to model file, i.e. yolov8n.pt, yolov8n.yaml                    |
-| `imgsz`     | `640`   | image size as scalar or (h, w) list, i.e. (640, 480)                 |
-| `half`      | `False` | FP16 quantization                                                    |
-| `int8`      | `False` | INT8 quantization                                                    |
-| `device`    | `None`  | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
-| `hard_fail` | `False` | do not continue on error (bool), or val floor threshold (float)      |
+| Key       | Value   | Description                                                           |
+|-----------|---------|-----------------------------------------------------------------------|
+| `model`   | `None`  | path to model file, i.e. yolov8n.pt, yolov8n.yaml                     |
+| `data`    | `None`  | path to YAML referencing the benchmarking dataset (under `val` label) |
+| `imgsz`   | `640`   | image size as scalar or (h, w) list, i.e. (640, 480)                  |
+| `half`    | `False` | FP16 quantization                                                     |
+| `int8`    | `False` | INT8 quantization                                                     |
+| `device`  | `None`  | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu  |
+| `verbose` | `False` | do not continue on error (bool), or val floor threshold (float)       |
 
 ## Export Formats
 
 Benchmarks will attempt to run automatically on all possible export formats below.
 
-| Format                                                             | `format` Argument | Model                     | Metadata |
-|--------------------------------------------------------------------|-------------------|---------------------------|----------|
-| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        |
-| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        |
-| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        |
-| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        |
-| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlmodel`         | ✅        |
-| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        |
-| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        |
-| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        |
-| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        |
-| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        |
-| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        |
+| Format                                                             | `format` Argument | Model                     | Metadata | Arguments                                           |
+|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|
+| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
+| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
+| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
+| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`                                     |
+| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
+| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
+| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`                                    |
+| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`                                             |
+| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`                             |
+| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
+| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`                                             |
+| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
+| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |
 
-See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.
+See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

downloads/ultralytics-main/docs/modes/export.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
+description: Step-by-step guide on exporting your YOLOv8 models to various format like ONNX, TensorRT, CoreML and more for deployment. Explore now!.
+keywords: YOLO, YOLOv8, Ultralytics, Model export, ONNX, TensorRT, CoreML, TensorFlow SavedModel, OpenVINO, PyTorch, export model
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@@ -22,19 +23,19 @@ export arguments.
 !!! example ""
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('yolov8n.pt')  # load an official model
         model = YOLO('path/to/best.pt')  # load a custom trained
-        
+
         # Export the model
         model.export(format='onnx')
         ```
     === "CLI"
-    
+
         ```bash
         yolo export model=yolov8n.pt format=onnx  # export official model
         yolo export model=path/to/best.pt format=onnx  # export custom trained model
@@ -77,10 +78,11 @@ i.e. `format='onnx'` or `format='engine'`.
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
 | [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`                                     |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
-| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlmodel`         | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
+| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`                                    |
 | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`                                             |
 | [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`                             |
 | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
 | [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`                                             |
-| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
+| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
+| [ncnn](https://github.com/Tencent/ncnn)                            | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`                                     |

downloads/ultralytics-main/docs/modes/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
-description: Use Ultralytics YOLOv8 Modes (Train, Val, Predict, Export, Track, Benchmark) to train, validate, predict, track, export or benchmark.
+description: From training to tracking, make the most of YOLOv8 with Ultralytics. Get insights and examples for each supported mode including validation, export, and benchmarking.
+keywords: Ultralytics, YOLOv8, Machine Learning, Object Detection, Training, Validation, Prediction, Export, Tracking, Benchmarking
 ---
 
 # Ultralytics YOLOv8 Modes
@@ -9,12 +10,12 @@ description: Use Ultralytics YOLOv8 Modes (Train, Val, Predict, Export, Track, B
 
 Ultralytics YOLOv8 supports several **modes** that can be used to perform different tasks. These modes are:
 
-**Train**: For training a YOLOv8 model on a custom dataset.  
-**Val**: For validating a YOLOv8 model after it has been trained.  
-**Predict**: For making predictions using a trained YOLOv8 model on new images or videos.  
-**Export**: For exporting a YOLOv8 model to a format that can be used for deployment.  
-**Track**: For tracking objects in real-time using a YOLOv8 model.  
-**Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.
+- **Train**: For training a YOLOv8 model on a custom dataset.
+- **Val**: For validating a YOLOv8 model after it has been trained.
+- **Predict**: For making predictions using a trained YOLOv8 model on new images or videos.
+- **Export**: For exporting a YOLOv8 model to a format that can be used for deployment.
+- **Track**: For tracking objects in real-time using a YOLOv8 model.
+- **Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.
 
 ## [Train](train.md)
 
@@ -64,4 +65,4 @@ or `accuracy_top5` metrics (for classification), and the inference time in milli
 formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
 their specific use case based on their requirements for speed and accuracy.
 
-[Benchmark Examples](benchmark.md){ .md-button .md-button--primary}
+[Benchmark Examples](benchmark.md){ .md-button .md-button--primary}

downloads/ultralytics-main/docs/modes/predict.md

@@ -1,106 +1,336 @@
 ---
 comments: true
-description: Get started with YOLOv8 Predict mode and input sources. Accepts various input sources such as images, videos, and directories.
+description: Discover how to use YOLOv8 predict mode for various tasks. Learn about different inference sources like images, videos, and data formats.
+keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks, streaming mode, image processing, video processing, machine learning, AI
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
 
-YOLOv8 **predict mode** can generate predictions for various tasks, returning either a list of `Results` objects or a
-memory-efficient generator of `Results` objects when using the streaming mode. Enable streaming mode by
-passing `stream=True` in the predictor's call method.
+YOLOv8 **predict mode** can generate predictions for various tasks, returning either a list of `Results` objects or a memory-efficient generator of `Results` objects when using the streaming mode. Enable streaming mode by passing `stream=True` in the predictor's call method.
 
 !!! example "Predict"
 
-    === "Return a list with `Stream=False`"
+    === "Return a list with `stream=False`"
         ```python
-        inputs = [img, img]  # list of numpy arrays
-        results = model(inputs)  # list of Results objects
-        
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # pretrained YOLOv8n model
+
+        # Run batched inference on a list of images
+        results = model(['im1.jpg', 'im2.jpg'])  # return a list of Results objects
+
+        # Process results list
         for result in results:
             boxes = result.boxes  # Boxes object for bbox outputs
             masks = result.masks  # Masks object for segmentation masks outputs
-            probs = result.probs  # Class probabilities for classification outputs
+            keypoints = result.keypoints  # Keypoints object for pose outputs
+            probs = result.probs  # Probs object for classification outputs
         ```
 
-    === "Return a generator with `Stream=True`"
+    === "Return a generator with `stream=True`"
         ```python
-        inputs = [img, img]  # list of numpy arrays
-        results = model(inputs, stream=True)  # generator of Results objects
-        
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # pretrained YOLOv8n model
+
+        # Run batched inference on a list of images
+        results = model(['im1.jpg', 'im2.jpg'], stream=True)  # return a generator of Results objects
+
+        # Process results generator
         for result in results:
             boxes = result.boxes  # Boxes object for bbox outputs
             masks = result.masks  # Masks object for segmentation masks outputs
-            probs = result.probs  # Class probabilities for classification outputs
+            keypoints = result.keypoints  # Keypoints object for pose outputs
+            probs = result.probs  # Probs object for classification outputs
         ```
 
+## Inference Sources
+
+YOLOv8 can process different types of input sources for inference, as shown in the table below. The sources include static images, video streams, and various data formats. The table also indicates whether each source can be used in streaming mode with the argument `stream=True` ✅. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory.
+
 !!! tip "Tip"
 
-    Streaming mode with `stream=True` should be used for long videos or large predict sources, otherwise results will accumuate in memory and will eventually cause out-of-memory errors. 
+    Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.
 
-## Sources
+| Source        | Argument                                   | Type            | Notes                                                                                       |
+|---------------|--------------------------------------------|-----------------|---------------------------------------------------------------------------------------------|
+| image         | `'image.jpg'`                              | `str` or `Path` | Single image file.                                                                          |
+| URL           | `'https://ultralytics.com/images/bus.jpg'` | `str`           | URL to an image.                                                                            |
+| screenshot    | `'screen'`                                 | `str`           | Capture a screenshot.                                                                       |
+| PIL           | `Image.open('im.jpg')`                     | `PIL.Image`     | HWC format with RGB channels.                                                               |
+| OpenCV        | `cv2.imread('im.jpg')`                     | `np.ndarray`    | HWC format with BGR channels `uint8 (0-255)`.                                               |
+| numpy         | `np.zeros((640,1280,3))`                   | `np.ndarray`    | HWC format with BGR channels `uint8 (0-255)`.                                               |
+| torch         | `torch.zeros(16,3,320,640)`                | `torch.Tensor`  | BCHW format with RGB channels `float32 (0.0-1.0)`.                                          |
+| CSV           | `'sources.csv'`                            | `str` or `Path` | CSV file containing paths to images, videos, or directories.                                |
+| video ✅       | `'video.mp4'`                              | `str` or `Path` | Video file in formats like MP4, AVI, etc.                                                   |
+| directory ✅   | `'path/'`                                  | `str` or `Path` | Path to a directory containing images or videos.                                            |
+| glob ✅        | `'path/*.jpg'`                             | `str`           | Glob pattern to match multiple files. Use the `*` character as a wildcard.                  |
+| YouTube ✅     | `'https://youtu.be/Zgi9g1ksQHc'`           | `str`           | URL to a YouTube video.                                                                     |
+| stream ✅      | `'rtsp://example.com/media.mp4'`           | `str`           | URL for streaming protocols such as RTSP, RTMP, or an IP address.                           |
+| multi-stream ✅ | `'list.streams'`                           | `str` or `Path` | `*.streams` text file with one stream URL per row, i.e. 8 streams will run at batch-size 8. |
 
-YOLOv8 can accept various input sources, as shown in the table below. This includes images, URLs, PIL images, OpenCV,
-numpy arrays, torch tensors, CSV files, videos, directories, globs, YouTube videos, and streams. The table indicates
-whether each source can be used in streaming mode with `stream=True` ✅ and an example argument for each source.
+Below are code examples for using each source type:
 
-| source      | model(arg)                                 | type           | notes            |
-|-------------|--------------------------------------------|----------------|------------------|
-| image       | `'im.jpg'`                                 | `str`, `Path`  |                  |
-| URL         | `'https://ultralytics.com/images/bus.jpg'` | `str`          |                  |
-| screenshot  | `'screen'`                                 | `str`          |                  |
-| PIL         | `Image.open('im.jpg')`                     | `PIL.Image`    | HWC, RGB         |
-| OpenCV      | `cv2.imread('im.jpg')`                     | `np.ndarray`   | HWC, BGR         |
-| numpy       | `np.zeros((640,1280,3))`                   | `np.ndarray`   | HWC              |
-| torch       | `torch.zeros(16,3,320,640)`                | `torch.Tensor` | BCHW, RGB        |
-| CSV         | `'sources.csv'`                            | `str`, `Path`  | RTSP, RTMP, HTTP |         
-| video ✅     | `'vid.mp4'`                                | `str`, `Path`  |                  |
-| directory ✅ | `'path/'`                                  | `str`, `Path`  |                  |
-| glob ✅      | `'path/*.jpg'`                             | `str`          | Use `*` operator |
-| YouTube ✅   | `'https://youtu.be/Zgi9g1ksQHc'`           | `str`          |                  |
-| stream ✅    | `'rtsp://example.com/media.mp4'`           | `str`          | RTSP, RTMP, HTTP |
+!!! example "Prediction sources"
 
-## Arguments
+    === "image"
+        Run inference on an image file.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define path to the image file
+        source = 'path/to/image.jpg'
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "screenshot"
+        Run inference on the current screen content as a screenshot.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define current screenshot as source
+        source = 'screen'
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "URL"
+        Run inference on an image or video hosted remotely via URL.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define remote image or video URL
+        source = 'https://ultralytics.com/images/bus.jpg'
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "PIL"
+        Run inference on an image opened with Python Imaging Library (PIL).
+        ```python
+        from PIL import Image
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Open an image using PIL
+        source = Image.open('path/to/image.jpg')
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "OpenCV"
+        Run inference on an image read with OpenCV.
+        ```python
+        import cv2
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Read an image using OpenCV
+        source = cv2.imread('path/to/image.jpg')
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "numpy"
+        Run inference on an image represented as a numpy array.
+        ```python
+        import numpy as np
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Create a random numpy array of HWC shape (640, 640, 3) with values in range [0, 255] and type uint8
+        source = np.random.randint(low=0, high=255, size=(640, 640, 3), dtype='uint8')
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "torch"
+        Run inference on an image represented as a PyTorch tensor.
+        ```python
+        import torch
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32
+        source = torch.rand(1, 3, 640, 640, dtype=torch.float32)
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "CSV"
+        Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
+        ```python
+        import torch
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define a path to a CSV file with images, URLs, videos and directories
+        source = 'path/to/file.csv'
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+        ```
+
+    === "video"
+        Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define path to video file
+        source = 'path/to/video.mp4'
+
+        # Run inference on the source
+        results = model(source, stream=True)  # generator of Results objects
+        ```
+
+    === "directory"
+        Run inference on all images and videos in a directory. To also capture images and videos in subdirectories use a glob pattern, i.e. `path/to/dir/**/*`.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define path to directory containing images and videos for inference
+        source = 'path/to/dir'
+
+        # Run inference on the source
+        results = model(source, stream=True)  # generator of Results objects
+        ```
+
+    === "glob"
+        Run inference on all images and videos that match a glob expression with `*` characters.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define a glob search for all JPG files in a directory
+        source = 'path/to/dir/*.jpg'
+
+        # OR define a recursive glob search for all JPG files including subdirectories
+        source = 'path/to/dir/**/*.jpg'
+
+        # Run inference on the source
+        results = model(source, stream=True)  # generator of Results objects
+        ```
+
+    === "YouTube"
+        Run inference on a YouTube video. By using `stream=True`, you can create a generator of Results objects to reduce memory usage for long videos.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Define source as YouTube video URL
+        source = 'https://youtu.be/Zgi9g1ksQHc'
+
+        # Run inference on the source
+        results = model(source, stream=True)  # generator of Results objects
+        ```
+
+    === "Streams"
+        Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols. If mutliple streams are provided in a `*.streams` text file then batched inference will run, i.e. 8 streams will run at batch-size 8, otherwise single streams will run at batch-size 1.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Single stream with batch-size 1 inference
+        source = 'rtsp://example.com/media.mp4'  # RTSP, RTMP or IP streaming address
+
+        # Multiple streams with batched inference (i.e. batch-size 8 for 8 streams)
+        source = 'path/to/list.streams'  # *.streams text file with one streaming address per row
+
+        # Run inference on the source
+        results = model(source, stream=True)  # generator of Results objects
+        ```
+
+## Inference Arguments
+
+`model.predict()` accepts multiple arguments that can be passed at inference time to override defaults:
 
-`model.predict` accepts multiple arguments that control the prediction operation. These arguments can be passed directly to `model.predict`:
 !!! example
 
-    ```
-    model.predict(source, save=True, imgsz=320, conf=0.5)
+    ```python
+    from ultralytics import YOLO
+
+    # Load a pretrained YOLOv8n model
+    model = YOLO('yolov8n.pt')
+
+    # Run inference on 'bus.jpg' with arguments
+    model.predict('bus.jpg', save=True, imgsz=320, conf=0.5)
     ```
 
 All supported arguments:
 
-| Key            | Value                  | Description                                                                    |
-|----------------|------------------------|--------------------------------------------------------------------------------|
-| `source`       | `'ultralytics/assets'` | source directory for images or videos                                          |
-| `conf`         | `0.25`                 | object confidence threshold for detection                                      |
-| `iou`          | `0.7`                  | intersection over union (IoU) threshold for NMS                                |
-| `half`         | `False`                | use half precision (FP16)                                                      |
-| `device`       | `None`                 | device to run on, i.e. cuda device=0/1/2/3 or device=cpu                       |
-| `show`         | `False`                | show results if possible                                                       |
-| `save`         | `False`                | save images with results                                                       |
-| `save_txt`     | `False`                | save results as .txt file                                                      |
-| `save_conf`    | `False`                | save results with confidence scores                                            |
-| `save_crop`    | `False`                | save cropped images with results                                               |
-| `hide_labels`  | `False`                | hide labels                                                                    |
-| `hide_conf`    | `False`                | hide confidence scores                                                         |
-| `max_det`      | `300`                  | maximum number of detections per image                                         |
-| `vid_stride`   | `False`                | video frame-rate stride                                                        |
-| `line_width`   | `None`                 | The line width of the bounding boxes. If None, it is scaled to the image size. |
-| `visualize`    | `False`                | visualize model features                                                       |
-| `augment`      | `False`                | apply image augmentation to prediction sources                                 |
-| `agnostic_nms` | `False`                | class-agnostic NMS                                                             |
-| `retina_masks` | `False`                | use high-resolution segmentation masks                                         |
-| `classes`      | `None`                 | filter results by class, i.e. class=0, or class=[0,2,3]                        |
-| `boxes`        | `True`                 | Show boxes in segmentation predictions                                         |
+| Name           | Type           | Default                | Description                                                                    |
+|----------------|----------------|------------------------|--------------------------------------------------------------------------------|
+| `source`       | `str`          | `'ultralytics/assets'` | source directory for images or videos                                          |
+| `conf`         | `float`        | `0.25`                 | object confidence threshold for detection                                      |
+| `iou`          | `float`        | `0.7`                  | intersection over union (IoU) threshold for NMS                                |
+| `imgsz`        | `int or tuple` | `640`                  | image size as scalar or (h, w) list, i.e. (640, 480)                           |
+| `half`         | `bool`         | `False`                | use half precision (FP16)                                                      |
+| `device`       | `None or str`  | `None`                 | device to run on, i.e. cuda device=0/1/2/3 or device=cpu                       |
+| `show`         | `bool`         | `False`                | show results if possible                                                       |
+| `save`         | `bool`         | `False`                | save images with results                                                       |
+| `save_txt`     | `bool`         | `False`                | save results as .txt file                                                      |
+| `save_conf`    | `bool`         | `False`                | save results with confidence scores                                            |
+| `save_crop`    | `bool`         | `False`                | save cropped images with results                                               |
+| `hide_labels`  | `bool`         | `False`                | hide labels                                                                    |
+| `hide_conf`    | `bool`         | `False`                | hide confidence scores                                                         |
+| `max_det`      | `int`          | `300`                  | maximum number of detections per image                                         |
+| `vid_stride`   | `bool`         | `False`                | video frame-rate stride                                                        |
+| `line_width`   | `None or int`  | `None`                 | The line width of the bounding boxes. If None, it is scaled to the image size. |
+| `visualize`    | `bool`         | `False`                | visualize model features                                                       |
+| `augment`      | `bool`         | `False`                | apply image augmentation to prediction sources                                 |
+| `agnostic_nms` | `bool`         | `False`                | class-agnostic NMS                                                             |
+| `retina_masks` | `bool`         | `False`                | use high-resolution segmentation masks                                         |
+| `classes`      | `None or list` | `None`                 | filter results by class, i.e. classes=0, or classes=[0,2,3]                    |
+| `boxes`        | `bool`         | `True`                 | Show boxes in segmentation predictions                                         |
 
 ## Image and Video Formats
 
-YOLOv8 supports various image and video formats, as specified
-in [yolo/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/data/utils.py). See the
-tables below for the valid suffixes and example predict commands.
+YOLOv8 supports various image and video formats, as specified in [data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py). See the tables below for the valid suffixes and example predict commands.
 
-### Image Suffixes
+### Images
+
+The below table contains valid Ultralytics image formats.
 
 | Image Suffixes | Example Predict Command          | Reference                                                                     |
 |----------------|----------------------------------|-------------------------------------------------------------------------------|
@@ -115,7 +345,9 @@ tables below for the valid suffixes and example predict commands.
 | .webp          | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP)                                    |
 | .pfm           | `yolo predict source=image.pfm`  | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats)        |
 
-### Video Suffixes
+### Videos
+
+The below table contains valid Ultralytics video formats.
 
 | Video Suffixes | Example Predict Command          | Reference                                                                        |
 |----------------|----------------------------------|----------------------------------------------------------------------------------|
@@ -134,149 +366,280 @@ tables below for the valid suffixes and example predict commands.
 
 ## Working with Results
 
-The `Results` object contains the following components:
-
-- `Results.boxes`: `Boxes` object with properties and methods for manipulating bounding boxes
-- `Results.masks`: `Masks` object for indexing masks or getting segment coordinates
-- `Results.probs`: `torch.Tensor` containing class probabilities or logits
-- `Results.orig_img`: Original image loaded in memory
-- `Results.path`: `Path` containing the path to the input image
-
-Each result is composed of a `torch.Tensor` by default, which allows for easy manipulation:
+All Ultralytics `predict()` calls will return a list of `Results` objects:
 
 !!! example "Results"
 
     ```python
-    results = results.cuda()
-    results = results.cpu()
-    results = results.to('cpu')
-    results = results.numpy()
+    from ultralytics import YOLO
+    
+    # Load a pretrained YOLOv8n model
+    model = YOLO('yolov8n.pt')
+    
+    # Run inference on an image
+    results = model('bus.jpg')  # list of 1 Results object
+    results = model(['bus.jpg', 'zidane.jpg'])  # list of 2 Results objects
     ```
 
+`Results` objects have the following attributes:
+
+| Attribute    | Type                  | Description                                                                              |
+|--------------|-----------------------|------------------------------------------------------------------------------------------|
+| `orig_img`   | `numpy.ndarray`       | The original image as a numpy array.                                                     |
+| `orig_shape` | `tuple`               | The original image shape in (height, width) format.                                      |
+| `boxes`      | `Boxes, optional`     | A Boxes object containing the detection bounding boxes.                                  |
+| `masks`      | `Masks, optional`     | A Masks object containing the detection masks.                                           |
+| `probs`      | `Probs, optional`     | A Probs object containing probabilities of each class for classification task.           |
+| `keypoints`  | `Keypoints, optional` | A Keypoints object containing detected keypoints for each object.                        |
+| `speed`      | `dict`                | A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image. |
+| `names`      | `dict`                | A dictionary of class names.                                                             |
+| `path`       | `str`                 | The path to the image file.                                                              |
+
+`Results` objects have the following methods:
+
+| Method          | Return Type     | Description                                                                         |
+|-----------------|-----------------|-------------------------------------------------------------------------------------|
+| `__getitem__()` | `Results`       | Return a Results object for the specified index.                                    |
+| `__len__()`     | `int`           | Return the number of detections in the Results object.                              |
+| `update()`      | `None`          | Update the boxes, masks, and probs attributes of the Results object.                |
+| `cpu()`         | `Results`       | Return a copy of the Results object with all tensors on CPU memory.                 |
+| `numpy()`       | `Results`       | Return a copy of the Results object with all tensors as numpy arrays.               |
+| `cuda()`        | `Results`       | Return a copy of the Results object with all tensors on GPU memory.                 |
+| `to()`          | `Results`       | Return a copy of the Results object with tensors on the specified device and dtype. |
+| `new()`         | `Results`       | Return a new Results object with the same image, path, and names.                   |
+| `keys()`        | `List[str]`     | Return a list of non-empty attribute names.                                         |
+| `plot()`        | `numpy.ndarray` | Plots the detection results. Returns a numpy array of the annotated image.          |
+| `verbose()`     | `str`           | Return log string for each task.                                                    |
+| `save_txt()`    | `None`          | Save predictions into a txt file.                                                   |
+| `save_crop()`   | `None`          | Save cropped predictions to `save_dir/cls/file_name.jpg`.                           |
+| `tojson()`      | `None`          | Convert the object to JSON format.                                                  |
+
+For more details see the `Results` class [documentation](../reference/engine/results.md).
+
 ### Boxes
 
-`Boxes` object can be used to index, manipulate, and convert bounding boxes to different formats. Box format conversion
-operations are cached, meaning they're only calculated once per object, and those values are reused for future calls.
-
-- Indexing a `Boxes` object returns a `Boxes` object:
+`Boxes` object can be used to index, manipulate, and convert bounding boxes to different formats.
 
 !!! example "Boxes"
 
     ```python
-    results = model(img)
-    boxes = results[0].boxes
-    box = boxes[0]  # returns one box
-    box.xyxy
+    from ultralytics import YOLO
+    
+    # Load a pretrained YOLOv8n model
+    model = YOLO('yolov8n.pt')
+    
+    # Run inference on an image
+    results = model('bus.jpg')  # results list
+    
+    # View results
+    for r in results:
+        print(r.boxes)  # print the Boxes object containing the detection bounding boxes
     ```
 
-- Properties and conversions
+Here is a table for the `Boxes` class methods and properties, including their name, type, and description:
 
-!!! example "Boxes Properties"
+| Name      | Type                      | Description                                                        |
+|-----------|---------------------------|--------------------------------------------------------------------|
+| `cpu()`   | Method                    | Move the object to CPU memory.                                     |
+| `numpy()` | Method                    | Convert the object to a numpy array.                               |
+| `cuda()`  | Method                    | Move the object to CUDA memory.                                    |
+| `to()`    | Method                    | Move the object to the specified device.                           |
+| `xyxy`    | Property (`torch.Tensor`) | Return the boxes in xyxy format.                                   |
+| `conf`    | Property (`torch.Tensor`) | Return the confidence values of the boxes.                         |
+| `cls`     | Property (`torch.Tensor`) | Return the class values of the boxes.                              |
+| `id`      | Property (`torch.Tensor`) | Return the track IDs of the boxes (if available).                  |
+| `xywh`    | Property (`torch.Tensor`) | Return the boxes in xywh format.                                   |
+| `xyxyn`   | Property (`torch.Tensor`) | Return the boxes in xyxy format normalized by original image size. |
+| `xywhn`   | Property (`torch.Tensor`) | Return the boxes in xywh format normalized by original image size. |
 
-    ```python
-    boxes.xyxy  # box with xyxy format, (N, 4)
-    boxes.xywh  # box with xywh format, (N, 4)
-    boxes.xyxyn  # box with xyxy format but normalized, (N, 4)
-    boxes.xywhn  # box with xywh format but normalized, (N, 4)
-    boxes.conf  # confidence score, (N, 1)
-    boxes.cls  # cls, (N, 1)
-    boxes.data  # raw bboxes tensor, (N, 6) or boxes.boxes
-    ```
+For more details see the `Boxes` class [documentation](../reference/engine/results.md).
 
 ### Masks
 
-`Masks` object can be used index, manipulate and convert masks to segments. The segment conversion operation is cached.
+`Masks` object can be used index, manipulate and convert masks to segments.
 
 !!! example "Masks"
 
     ```python
-    results = model(inputs)
-    masks = results[0].masks  # Masks object
-    masks.xy  # x, y segments (pixels), List[segment] * N
-    masks.xyn  # x, y segments (normalized), List[segment] * N
-    masks.data  # raw masks tensor, (N, H, W) or masks.masks 
+    from ultralytics import YOLO
+    
+    # Load a pretrained YOLOv8n-seg Segment model
+    model = YOLO('yolov8n-seg.pt')
+    
+    # Run inference on an image
+    results = model('bus.jpg')  # results list
+    
+    # View results
+    for r in results:
+        print(r.masks)  # print the Masks object containing the detected instance masks
     ```
 
-### probs
+Here is a table for the `Masks` class methods and properties, including their name, type, and description:
 
-`probs` attribute of `Results` class is a `Tensor` containing class probabilities of a classification operation.
+| Name      | Type                      | Description                                                     |
+|-----------|---------------------------|-----------------------------------------------------------------|
+| `cpu()`   | Method                    | Returns the masks tensor on CPU memory.                         |
+| `numpy()` | Method                    | Returns the masks tensor as a numpy array.                      |
+| `cuda()`  | Method                    | Returns the masks tensor on GPU memory.                         |
+| `to()`    | Method                    | Returns the masks tensor with the specified device and dtype.   |
+| `xyn`     | Property (`torch.Tensor`) | A list of normalized segments represented as tensors.           |
+| `xy`      | Property (`torch.Tensor`) | A list of segments in pixel coordinates represented as tensors. |
+
+For more details see the `Masks` class [documentation](../reference/engine/results.md).
+
+### Keypoints
+
+`Keypoints` object can be used index, manipulate and normalize coordinates.
+
+!!! example "Keypoints"
+
+    ```python
+    from ultralytics import YOLO
+    
+    # Load a pretrained YOLOv8n-pose Pose model
+    model = YOLO('yolov8n-pose.pt')
+    
+    # Run inference on an image
+    results = model('bus.jpg')  # results list
+    
+    # View results
+    for r in results:
+        print(r.keypoints)  # print the Keypoints object containing the detected keypoints
+    ```
+
+Here is a table for the `Keypoints` class methods and properties, including their name, type, and description:
+
+| Name      | Type                      | Description                                                       |
+|-----------|---------------------------|-------------------------------------------------------------------|
+| `cpu()`   | Method                    | Returns the keypoints tensor on CPU memory.                       |
+| `numpy()` | Method                    | Returns the keypoints tensor as a numpy array.                    |
+| `cuda()`  | Method                    | Returns the keypoints tensor on GPU memory.                       |
+| `to()`    | Method                    | Returns the keypoints tensor with the specified device and dtype. |
+| `xyn`     | Property (`torch.Tensor`) | A list of normalized keypoints represented as tensors.            |
+| `xy`      | Property (`torch.Tensor`) | A list of keypoints in pixel coordinates represented as tensors.  |
+| `conf`    | Property (`torch.Tensor`) | Returns confidence values of keypoints if available, else None.   |
+
+For more details see the `Keypoints` class [documentation](../reference/engine/results.md).
+
+### Probs
+
+`Probs` object can be used index, get `top1` and `top5` indices and scores of classification.
 
 !!! example "Probs"
 
     ```python
-    results = model(inputs)
-    results[0].probs  # cls prob, (num_class, )
+    from ultralytics import YOLO
+    
+    # Load a pretrained YOLOv8n-cls Classify model
+    model = YOLO('yolov8n-cls.pt')
+    
+    # Run inference on an image
+    results = model('bus.jpg')  # results list
+    
+    # View results
+    for r in results:
+        print(r.probs)  # print the Probs object containing the detected class probabilities
     ```
 
-Class reference documentation for `Results` module and its components can be found [here](../reference/yolo/engine/results.md)
+Here's a table summarizing the methods and properties for the `Probs` class:
 
-## Plotting results
+| Name       | Type                      | Description                                                             |
+|------------|---------------------------|-------------------------------------------------------------------------|
+| `cpu()`    | Method                    | Returns a copy of the probs tensor on CPU memory.                       |
+| `numpy()`  | Method                    | Returns a copy of the probs tensor as a numpy array.                    |
+| `cuda()`   | Method                    | Returns a copy of the probs tensor on GPU memory.                       |
+| `to()`     | Method                    | Returns a copy of the probs tensor with the specified device and dtype. |
+| `top1`     | Property (`int`)          | Index of the top 1 class.                                               |
+| `top5`     | Property (`list[int]`)    | Indices of the top 5 classes.                                           |
+| `top1conf` | Property (`torch.Tensor`) | Confidence of the top 1 class.                                          |
+| `top5conf` | Property (`torch.Tensor`) | Confidences of the top 5 classes.                                       |
 
-You can use `plot()` function of `Result` object to plot results on in image object. It plots all components(boxes,
-masks, classification logits, etc.) found in the results object
+For more details see the `Probs` class [documentation](../reference/engine/results.md).
+
+## Plotting Results
+
+You can use the `plot()` method of a `Result` objects to visualize predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the `Results` object onto a numpy array that can then be shown or saved.
 
 !!! example "Plotting"
 
     ```python
-    res = model(img)
-    res_plotted = res[0].plot()
-    cv2.imshow("result", res_plotted)
-    ```
+    from PIL import Image
+    from ultralytics import YOLO
 
-| Argument                      | Description                                                                            |
-|-------------------------------|----------------------------------------------------------------------------------------|
-| `conf (bool)`                 | Whether to plot the detection confidence score.                                        |
-| `line_width (int, optional)`  | The line width of the bounding boxes. If None, it is scaled to the image size.         |
-| `font_size (float, optional)` | The font size of the text. If None, it is scaled to the image size.                    |
-| `font (str)`                  | The font to use for the text.                                                          |
-| `pil (bool)`                  | Whether to use PIL for image plotting.                                                 |
-| `example (str)`               | An example string to display. Useful for indicating the expected format of the output. |
-| `img (numpy.ndarray)`         | Plot to another image. if not, plot to original image.                                 |
-| `labels (bool)`               | Whether to plot the label of bounding boxes.                                           |
-| `boxes (bool)`                | Whether to plot the bounding boxes.                                                    |
-| `masks (bool)`                | Whether to plot the masks.                                                             |
-| `probs (bool)`                | Whether to plot classification probability.                                            |
+    # Load a pretrained YOLOv8n model
+    model = YOLO('yolov8n.pt')
+
+    # Run inference on 'bus.jpg'
+    results = model('bus.jpg')  # results list
+
+    # Show the results
+    for r in results:
+        im_array = r.plot()  # plot a BGR numpy array of predictions
+        im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+        im.show()  # show image
+        im.save('results.jpg')  # save image
+    ```
+    
+    The `plot()` method supports the following arguments:
+    
+    | Argument     | Type            | Description                                                                    | Default       |
+    |--------------|-----------------|--------------------------------------------------------------------------------|---------------|
+    | `conf`       | `bool`          | Whether to plot the detection confidence score.                                | `True`        |
+    | `line_width` | `float`         | The line width of the bounding boxes. If None, it is scaled to the image size. | `None`        |
+    | `font_size`  | `float`         | The font size of the text. If None, it is scaled to the image size.            | `None`        |
+    | `font`       | `str`           | The font to use for the text.                                                  | `'Arial.ttf'` |
+    | `pil`        | `bool`          | Whether to return the image as a PIL Image.                                    | `False`       |
+    | `img`        | `numpy.ndarray` | Plot to another image. if not, plot to original image.                         | `None`        |
+    | `im_gpu`     | `torch.Tensor`  | Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting. | `None`        |
+    | `kpt_radius` | `int`           | Radius of the drawn keypoints. Default is 5.                                   | `5`           |
+    | `kpt_line`   | `bool`          | Whether to draw lines connecting keypoints.                                    | `True`        |
+    | `labels`     | `bool`          | Whether to plot the label of bounding boxes.                                   | `True`        |
+    | `boxes`      | `bool`          | Whether to plot the bounding boxes.                                            | `True`        |
+    | `masks`      | `bool`          | Whether to plot the masks.                                                     | `True`        |
+    | `probs`      | `bool`          | Whether to plot classification probability                                     | `True`        |
 
 ## Streaming Source `for`-loop
 
-Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video frames. This script assumes you have already installed the necessary packages (opencv-python and ultralytics).
+Here's a Python script using OpenCV (`cv2`) and YOLOv8 to run inference on video frames. This script assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`).
 
 !!! example "Streaming for-loop"
 
     ```python
     import cv2
     from ultralytics import YOLO
-    
+
     # Load the YOLOv8 model
     model = YOLO('yolov8n.pt')
-    
+
     # Open the video file
     video_path = "path/to/your/video/file.mp4"
     cap = cv2.VideoCapture(video_path)
-    
+
     # Loop through the video frames
     while cap.isOpened():
         # Read a frame from the video
         success, frame = cap.read()
-    
+
         if success:
             # Run YOLOv8 inference on the frame
             results = model(frame)
-    
+
             # Visualize the results on the frame
             annotated_frame = results[0].plot()
-    
+
             # Display the annotated frame
             cv2.imshow("YOLOv8 Inference", annotated_frame)
-    
+
             # Break the loop if 'q' is pressed
             if cv2.waitKey(1) & 0xFF == ord("q"):
                 break
         else:
             # Break the loop if the end of the video is reached
             break
-    
+
     # Release the video capture object and close the display window
     cap.release()
     cv2.destroyAllWindows()
     ```
+
+This script will run predictions on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.

downloads/ultralytics-main/docs/modes/track.md

@@ -1,101 +1,286 @@
 ---
 comments: true
-description: Explore YOLOv8n-based object tracking with Ultralytics' BoT-SORT and ByteTrack. Learn configuration, usage, and customization tips.
+description: Learn how to use Ultralytics YOLO for object tracking in video streams. Guides to use different trackers and customise tracker configurations.
+keywords: Ultralytics, YOLO, object tracking, video streams, BoT-SORT, ByteTrack, Python guide, CLI guide
 ---
 
-<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
+<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png">
 
-Object tracking is a task that involves identifying the location and class of objects, then assigning a unique ID to
-that detection in video streams.
+Object tracking is a task that involves identifying the location and class of objects, then assigning a unique ID to that detection in video streams.
 
 The output of tracker is the same as detection with an added object ID.
 
 ## Available Trackers
 
-The following tracking algorithms have been implemented and can be enabled by passing `tracker=tracker_type.yaml`
+Ultralytics YOLO supports the following tracking algorithms. They can be enabled by passing the relevant YAML configuration file such as `tracker=tracker_type.yaml`:
 
-* [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - `botsort.yaml`
-* [ByteTrack](https://github.com/ifzhang/ByteTrack) - `bytetrack.yaml`
+* [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - Use `botsort.yaml` to enable this tracker.
+* [ByteTrack](https://github.com/ifzhang/ByteTrack) - Use `bytetrack.yaml` to enable this tracker.
 
 The default tracker is BoT-SORT.
 
 ## Tracking
 
-Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
+To run the tracker on video streams, use a trained Detect, Segment or Pose model such as YOLOv8n, YOLOv8n-seg and YOLOv8n-pose.
 
 !!! example ""
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
-        # Load a model
-        model = YOLO('yolov8n.pt')  # load an official detection model
-        model = YOLO('yolov8n-seg.pt')  # load an official segmentation model
-        model = YOLO('path/to/best.pt')  # load a custom model
-        
-        # Track with the model
-        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True) 
-        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml") 
+
+        # Load an official or custom model
+        model = YOLO('yolov8n.pt')  # Load an official Detect model
+        model = YOLO('yolov8n-seg.pt')  # Load an official Segment model
+        model = YOLO('yolov8n-pose.pt')  # Load an official Pose model
+        model = YOLO('path/to/best.pt')  # Load a custom trained model
+
+        # Perform tracking with the model
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)  # Tracking with default tracker
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml")  # Tracking with ByteTrack tracker
         ```
+
     === "CLI"
-    
-        ```bash
-        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc"  # official detection model
-        yolo track model=yolov8n-seg.pt source=...   # official segmentation model
-        yolo track model=path/to/best.pt source=...  # custom model
-        yolo track model=path/to/best.pt  tracker="bytetrack.yaml" # bytetrack tracker
 
+        ```bash
+        # Perform tracking with various models using the command line interface
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc"  # Official Detect model
+        yolo track model=yolov8n-seg.pt source="https://youtu.be/Zgi9g1ksQHc"  # Official Segment model
+        yolo track model=yolov8n-pose.pt source="https://youtu.be/Zgi9g1ksQHc"  # Official Pose model
+        yolo track model=path/to/best.pt source="https://youtu.be/Zgi9g1ksQHc"  # Custom trained model
+
+        # Track using ByteTrack tracker
+        yolo track model=path/to/best.pt tracker="bytetrack.yaml" 
         ```
 
-As in the above usage, we support both the detection and segmentation models for tracking and the only thing you need to
-do is loading the corresponding (detection or segmentation) model.
+As can be seen in the above usage, tracking is available for all Detect, Segment and Pose models run on videos or streaming sources.
 
 ## Configuration
 
-### Tracking
+### Tracking Arguments
+
+Tracking configuration shares properties with Predict mode, such as `conf`, `iou`, and `show`. For further configurations, refer to the [Predict](https://docs.ultralytics.com/modes/predict/) model page.
 
-Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer
-to [predict page](https://docs.ultralytics.com/modes/predict/).
 !!! example ""
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
+        # Configure the tracking parameters and run the tracker
         model = YOLO('yolov8n.pt')
-        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True) 
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
         ```
+
     === "CLI"
-    
+
         ```bash
+        # Configure tracking parameters and run the tracker using the command line interface
         yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show
-
         ```
 
-### Tracker
+### Tracker Selection
+
+Ultralytics also allows you to use a modified tracker configuration file. To do this, simply make a copy of a tracker config file (for example, `custom_tracker.yaml`) from [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) and modify any configurations (except the `tracker_type`) as per your needs.
 
-We also support using a modified tracker config file, just copy a config file i.e `custom_tracker.yaml`
-from [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) and modify
-any configurations(expect the `tracker_type`) you need to.
 !!! example ""
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
+        # Load the model and run the tracker with a custom configuration file
         model = YOLO('yolov8n.pt')
-        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml') 
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml')
         ```
+
     === "CLI"
-    
+
         ```bash
+        # Load the model and run the tracker with a custom configuration file using the command line interface
         yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" tracker='custom_tracker.yaml'
         ```
 
-Please refer to [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg)
-page
+For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) page.
 
+## Python Examples
+
+### Persisting Tracks Loop
+
+Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`).
+
+!!! example "Streaming for-loop with tracking"
+
+    ```python
+    import cv2
+    from ultralytics import YOLO
+
+    # Load the YOLOv8 model
+    model = YOLO('yolov8n.pt')
+
+    # Open the video file
+    video_path = "path/to/video.mp4"
+    cap = cv2.VideoCapture(video_path)
+
+    # Loop through the video frames
+    while cap.isOpened():
+        # Read a frame from the video
+        success, frame = cap.read()
+
+        if success:
+            # Run YOLOv8 tracking on the frame, persisting tracks between frames
+            results = model.track(frame, persist=True)
+
+            # Visualize the results on the frame
+            annotated_frame = results[0].plot()
+
+            # Display the annotated frame
+            cv2.imshow("YOLOv8 Tracking", annotated_frame)
+
+            # Break the loop if 'q' is pressed
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+        else:
+            # Break the loop if the end of the video is reached
+            break
+
+    # Release the video capture object and close the display window
+    cap.release()
+    cv2.destroyAllWindows()
+    ```
+
+Please note the change from `model(frame)` to `model.track(frame)`, which enables object tracking instead of simple detection. This modified script will run the tracker on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.
+
+### Plotting Tracks Over Time
+
+Visualizing object tracks over consecutive frames can provide valuable insights into the movement patterns and behavior of detected objects within a video. With Ultralytics YOLOv8, plotting these tracks is a seamless and efficient process.
+
+In the following example, we demonstrate how to utilize YOLOv8's tracking capabilities to plot the movement of detected objects across multiple video frames. This script involves opening a video file, reading it frame by frame, and utilizing the YOLO model to identify and track various objects. By retaining the center points of the detected bounding boxes and connecting them, we can draw lines that represent the paths followed by the tracked objects.
+
+!!! example "Plotting tracks over multiple video frames"
+
+    ```python
+    from collections import defaultdict
+    
+    import cv2
+    import numpy as np
+    
+    from ultralytics import YOLO
+    
+    # Load the YOLOv8 model
+    model = YOLO('yolov8n.pt')
+    
+    # Open the video file
+    video_path = "path/to/video.mp4"
+    cap = cv2.VideoCapture(video_path)
+    
+    # Store the track history
+    track_history = defaultdict(lambda: [])
+    
+    # Loop through the video frames
+    while cap.isOpened():
+        # Read a frame from the video
+        success, frame = cap.read()
+    
+        if success:
+            # Run YOLOv8 tracking on the frame, persisting tracks between frames
+            results = model.track(frame, persist=True)
+    
+            # Get the boxes and track IDs
+            boxes = results[0].boxes.xywh.cpu()
+            track_ids = results[0].boxes.id.int().cpu().tolist()
+    
+            # Visualize the results on the frame
+            annotated_frame = results[0].plot()
+    
+            # Plot the tracks
+            for box, track_id in zip(boxes, track_ids):
+                x, y, w, h = box
+                track = track_history[track_id]
+                track.append((float(x), float(y)))  # x, y center point
+                if len(track) > 30:  # retain 90 tracks for 90 frames
+                    track.pop(0)
+    
+                # Draw the tracking lines
+                points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+                cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+    
+            # Display the annotated frame
+            cv2.imshow("YOLOv8 Tracking", annotated_frame)
+    
+            # Break the loop if 'q' is pressed
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+        else:
+            # Break the loop if the end of the video is reached
+            break
+    
+    # Release the video capture object and close the display window
+    cap.release()
+    cv2.destroyAllWindows()
+    ```
+
+### Multithreaded Tracking
+
+Multithreaded tracking provides the capability to run object tracking on multiple video streams simultaneously. This is particularly useful when handling multiple video inputs, such as from multiple surveillance cameras, where concurrent processing can greatly enhance efficiency and performance.
+
+In the provided Python script, we make use of Python's `threading` module to run multiple instances of the tracker concurrently. Each thread is responsible for running the tracker on one video file, and all the threads run simultaneously in the background.
+
+To ensure that each thread receives the correct parameters (the video file and the model to use), we define a function `run_tracker_in_thread` that accepts these parameters and contains the main tracking loop. This function reads the video frame by frame, runs the tracker, and displays the results.
+
+Two different models are used in this example: `yolov8n.pt` and `yolov8n-seg.pt`, each tracking objects in a different video file. The video files are specified in `video_file1` and `video_file2`.
+
+The `daemon=True` parameter in `threading.Thread` means that these threads will be closed as soon as the main program finishes. We then start the threads with `start()` and use `join()` to make the main thread wait until both tracker threads have finished.
+
+Finally, after all threads have completed their task, the windows displaying the results are closed using `cv2.destroyAllWindows()`.
+
+!!! example "Streaming for-loop with tracking"
+
+    ```python
+    import threading
+    
+    import cv2
+    from ultralytics import YOLO
+    
+    
+    def run_tracker_in_thread(filename, model):
+        video = cv2.VideoCapture(filename)
+        frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+        for _ in range(frames):
+            ret, frame = video.read()
+            if ret:
+                results = model.track(source=frame, persist=True)
+                res_plotted = results[0].plot()
+                cv2.imshow('p', res_plotted)
+                if cv2.waitKey(1) == ord('q'):
+                    break
+    
+    
+    # Load the models
+    model1 = YOLO('yolov8n.pt')
+    model2 = YOLO('yolov8n-seg.pt')
+    
+    # Define the video files for the trackers
+    video_file1 = 'path/to/video1.mp4'
+    video_file2 = 'path/to/video2.mp4'
+    
+    # Create the tracker threads
+    tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1), daemon=True)
+    tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2), daemon=True)
+    
+    # Start the tracker threads
+    tracker_thread1.start()
+    tracker_thread2.start()
+    
+    # Wait for the tracker threads to finish
+    tracker_thread1.join()
+    tracker_thread2.join()
+    
+    # Clean up and close windows
+    cv2.destroyAllWindows()
+    ```
+
+This example can easily be extended to handle more video files and models by creating more threads and applying the same methodology.

downloads/ultralytics-main/docs/modes/train.md

@@ -1,13 +1,12 @@
 ---
 comments: true
-description: Learn how to train custom YOLOv8 models on various datasets, configure hyperparameters, and use Ultralytics' YOLO for seamless training.
+description: Step-by-step guide to train YOLOv8 models with Ultralytics YOLO with examples of single-GPU and multi-GPU training. Efficient way for object detection training.
+keywords: Ultralytics, YOLOv8, YOLO, object detection, train mode, custom dataset, GPU training, multi-GPU, hyperparameters, CLI examples, Python examples
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
 
-**Train mode** is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the
-specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can
-accurately predict the classes and locations of objects in an image.
+**Train mode** is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
 
 !!! tip "Tip"
 
@@ -15,26 +14,28 @@ accurately predict the classes and locations of objects in an image.
 
 ## Usage Examples
 
-Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Arguments section below for a full list of
-training arguments.
+Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. See Arguments section below for a full list of training arguments.
 
-!!! example ""
+!!! example "Single-GPU and CPU Training Example"
+
+    Device is determined automatically. If a GPU is available then it will be used, otherwise training will start on CPU.
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('yolov8n.yaml')  # build a new model from YAML
         model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
         model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
-        
+
         # Train the model
-        model.train(data='coco128.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco128.yaml', epochs=100, imgsz=640)
         ```
+
     === "CLI"
-    
+
         ```bash
         # Build a new model from YAML and start training from scratch
         yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
@@ -46,57 +47,213 @@ training arguments.
         yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
         ```
 
+### Multi-GPU Training
+
+The training device can be specified using the `device` argument. If no argument is passed GPU `device=0` will be used if available, otherwise `device=cpu` will be used.
+
+!!! example "Multi-GPU Training Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model with 2 GPUs
+        results = model.train(data='coco128.yaml', epochs=100, imgsz=640, device=[0, 1])
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model using GPUs 0 and 1
+        yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 device=0,1
+        ```
+
+### Apple M1 and M2 MPS Training
+
+With the support for Apple M1 and M2 chips integrated in the Ultralytics YOLO models, it's now possible to train your models on devices utilizing the powerful Metal Performance Shaders (MPS) framework. The MPS offers a high-performance way of executing computation and image processing tasks on Apple's custom silicon.
+
+To enable training on Apple M1 and M2 chips, you should specify 'mps' as your device when initiating the training process. Below is an example of how you could do this in Python and via the command line:
+
+!!! example "MPS Training Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model with 2 GPUs
+        results = model.train(data='coco128.yaml', epochs=100, imgsz=640, device='mps')
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model using GPUs 0 and 1
+        yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 device=mps
+        ```
+
+While leveraging the computational power of the M1/M2 chips, this enables more efficient processing of the training tasks. For more detailed guidance and advanced configuration options, please refer to the [PyTorch MPS documentation](https://pytorch.org/docs/stable/notes/mps.html).
+
+### Resuming Interrupted Trainings
+
+Resuming training from a previously saved state is a crucial feature when working with deep learning models. This can come in handy in various scenarios, like when the training process has been unexpectedly interrupted, or when you wish to continue training a model with new data or for more epochs.
+
+When training is resumed, Ultralytics YOLO loads the weights from the last saved model and also restores the optimizer state, learning rate scheduler, and the epoch number. This allows you to continue the training process seamlessly from where it was left off.
+
+You can easily resume training in Ultralytics YOLO by setting the `resume` argument to `True` when calling the `train` method, and specifying the path to the `.pt` file containing the partially trained model weights.
+
+Below is an example of how to resume an interrupted training using Python and via the command line:
+
+!!! example "Resume Training Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('path/to/last.pt')  # load a partially trained model
+
+        # Resume training
+        results = model.train(resume=True)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Resume an interrupted training
+        yolo train resume model=path/to/last.pt
+        ```
+
+By setting `resume=True`, the `train` function will continue training from where it left off, using the state stored in the 'path/to/last.pt' file. If the `resume` argument is omitted or set to `False`, the `train` function will start a new training session.
+
+Remember that checkpoints are saved at the end of every epoch by default, or at fixed interval using the `save_period` argument, so you must complete at least 1 epoch to resume a training run.
+
 ## Arguments
 
-Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a
-dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings
-include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process
-include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It
-is important to carefully tune and experiment with these settings to achieve the best possible performance for a given
-task.
+Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a given task.
 
-| Key               | Value    | Description                                                                 |
-|-------------------|----------|-----------------------------------------------------------------------------|
-| `model`           | `None`   | path to model file, i.e. yolov8n.pt, yolov8n.yaml                           |
-| `data`            | `None`   | path to data file, i.e. coco128.yaml                                        |
-| `epochs`          | `100`    | number of epochs to train for                                               |
-| `patience`        | `50`     | epochs to wait for no observable improvement for early stopping of training |
-| `batch`           | `16`     | number of images per batch (-1 for AutoBatch)                               |
-| `imgsz`           | `640`    | size of input images as integer or w,h                                      |
-| `save`            | `True`   | save train checkpoints and predict results                                  |
-| `save_period`     | `-1`     | Save checkpoint every x epochs (disabled if < 1)                            |
-| `cache`           | `False`  | True/ram, disk or False. Use cache for data loading                         |
-| `device`          | `None`   | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu        |
-| `workers`         | `8`      | number of worker threads for data loading (per RANK if DDP)                 |
-| `project`         | `None`   | project name                                                                |
-| `name`            | `None`   | experiment name                                                             |
-| `exist_ok`        | `False`  | whether to overwrite existing experiment                                    |
-| `pretrained`      | `False`  | whether to use a pretrained model                                           |
-| `optimizer`       | `'SGD'`  | optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']               |
-| `verbose`         | `False`  | whether to print verbose output                                             |
-| `seed`            | `0`      | random seed for reproducibility                                             |
-| `deterministic`   | `True`   | whether to enable deterministic mode                                        |
-| `single_cls`      | `False`  | train multi-class data as single-class                                      |
-| `rect`            | `False`  | rectangular training with each batch collated for minimum padding           |
-| `cos_lr`          | `False`  | use cosine learning rate scheduler                                          |
-| `close_mosaic`    | `0`      | (int) disable mosaic augmentation for final epochs                          |
-| `resume`          | `False`  | resume training from last checkpoint                                        |
-| `amp`             | `True`   | Automatic Mixed Precision (AMP) training, choices=[True, False]             |
-| `lr0`             | `0.01`   | initial learning rate (i.e. SGD=1E-2, Adam=1E-3)                            |
-| `lrf`             | `0.01`   | final learning rate (lr0 * lrf)                                             |
-| `momentum`        | `0.937`  | SGD momentum/Adam beta1                                                     |
-| `weight_decay`    | `0.0005` | optimizer weight decay 5e-4                                                 |
-| `warmup_epochs`   | `3.0`    | warmup epochs (fractions ok)                                                |
-| `warmup_momentum` | `0.8`    | warmup initial momentum                                                     |
-| `warmup_bias_lr`  | `0.1`    | warmup initial bias lr                                                      |
-| `box`             | `7.5`    | box loss gain                                                               |
-| `cls`             | `0.5`    | cls loss gain (scale with pixels)                                           |
-| `dfl`             | `1.5`    | dfl loss gain                                                               |
-| `pose`            | `12.0`   | pose loss gain (pose-only)                                                  |
-| `kobj`            | `2.0`    | keypoint obj loss gain (pose-only)                                          |
-| `label_smoothing` | `0.0`    | label smoothing (fraction)                                                  |
-| `nbs`             | `64`     | nominal batch size                                                          |
-| `overlap_mask`    | `True`   | masks should overlap during training (segment train only)                   |
-| `mask_ratio`      | `4`      | mask downsample ratio (segment train only)                                  |
-| `dropout`         | `0.0`    | use dropout regularization (classify train only)                            |
-| `val`             | `True`   | validate/test during training                                               |
+| Key               | Value    | Description                                                                                    |
+|-------------------|----------|------------------------------------------------------------------------------------------------|
+| `model`           | `None`   | path to model file, i.e. yolov8n.pt, yolov8n.yaml                                              |
+| `data`            | `None`   | path to data file, i.e. coco128.yaml                                                           |
+| `epochs`          | `100`    | number of epochs to train for                                                                  |
+| `patience`        | `50`     | epochs to wait for no observable improvement for early stopping of training                    |
+| `batch`           | `16`     | number of images per batch (-1 for AutoBatch)                                                  |
+| `imgsz`           | `640`    | size of input images as integer                                                                |
+| `save`            | `True`   | save train checkpoints and predict results                                                     |
+| `save_period`     | `-1`     | Save checkpoint every x epochs (disabled if < 1)                                               |
+| `cache`           | `False`  | True/ram, disk or False. Use cache for data loading                                            |
+| `device`          | `None`   | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu                           |
+| `workers`         | `8`      | number of worker threads for data loading (per RANK if DDP)                                    |
+| `project`         | `None`   | project name                                                                                   |
+| `name`            | `None`   | experiment name                                                                                |
+| `exist_ok`        | `False`  | whether to overwrite existing experiment                                                       |
+| `pretrained`      | `False`  | whether to use a pretrained model                                                              |
+| `optimizer`       | `'auto'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]              |
+| `verbose`         | `False`  | whether to print verbose output                                                                |
+| `seed`            | `0`      | random seed for reproducibility                                                                |
+| `deterministic`   | `True`   | whether to enable deterministic mode                                                           |
+| `single_cls`      | `False`  | train multi-class data as single-class                                                         |
+| `rect`            | `False`  | rectangular training with each batch collated for minimum padding                              |
+| `cos_lr`          | `False`  | use cosine learning rate scheduler                                                             |
+| `close_mosaic`    | `10`     | (int) disable mosaic augmentation for final epochs (0 to disable)                              |
+| `resume`          | `False`  | resume training from last checkpoint                                                           |
+| `amp`             | `True`   | Automatic Mixed Precision (AMP) training, choices=[True, False]                                |
+| `fraction`        | `1.0`    | dataset fraction to train on (default is 1.0, all images in train set)                         |
+| `profile`         | `False`  | profile ONNX and TensorRT speeds during training for loggers                                   |
+| `freeze`          | `None`   | (int or list, optional) freeze first n layers, or freeze list of layer indices during training |
+| `lr0`             | `0.01`   | initial learning rate (i.e. SGD=1E-2, Adam=1E-3)                                               |
+| `lrf`             | `0.01`   | final learning rate (lr0 * lrf)                                                                |
+| `momentum`        | `0.937`  | SGD momentum/Adam beta1                                                                        |
+| `weight_decay`    | `0.0005` | optimizer weight decay 5e-4                                                                    |
+| `warmup_epochs`   | `3.0`    | warmup epochs (fractions ok)                                                                   |
+| `warmup_momentum` | `0.8`    | warmup initial momentum                                                                        |
+| `warmup_bias_lr`  | `0.1`    | warmup initial bias lr                                                                         |
+| `box`             | `7.5`    | box loss gain                                                                                  |
+| `cls`             | `0.5`    | cls loss gain (scale with pixels)                                                              |
+| `dfl`             | `1.5`    | dfl loss gain                                                                                  |
+| `pose`            | `12.0`   | pose loss gain (pose-only)                                                                     |
+| `kobj`            | `2.0`    | keypoint obj loss gain (pose-only)                                                             |
+| `label_smoothing` | `0.0`    | label smoothing (fraction)                                                                     |
+| `nbs`             | `64`     | nominal batch size                                                                             |
+| `overlap_mask`    | `True`   | masks should overlap during training (segment train only)                                      |
+| `mask_ratio`      | `4`      | mask downsample ratio (segment train only)                                                     |
+| `dropout`         | `0.0`    | use dropout regularization (classify train only)                                               |
+| `val`             | `True`   | validate/test during training                                                                  |
+
+## Logging
+
+In training a YOLOv8 model, you might find it valuable to keep track of the model's performance over time. This is where logging comes into play. Ultralytics' YOLO provides support for three types of loggers - Comet, ClearML, and TensorBoard.
+
+To use a logger, select it from the dropdown menu in the code snippet above and run it. The chosen logger will be installed and initialized.
+
+### Comet
+
+[Comet](https://www.comet.ml/site/) is a platform that allows data scientists and developers to track, compare, explain and optimize experiments and models. It provides functionalities such as real-time metrics, code diffs, and hyperparameters tracking.
+
+To use Comet:
+
+!!! example ""
+
+    === "Python"
+        ```python
+        # pip install comet_ml
+        import comet_ml
+        
+        comet_ml.init()
+        ```
+
+Remember to sign in to your Comet account on their website and get your API key. You will need to add this to your environment variables or your script to log your experiments.
+
+### ClearML
+
+[ClearML](https://www.clear.ml/) is an open-source platform that automates tracking of experiments and helps with efficient sharing of resources. It is designed to help teams manage, execute, and reproduce their ML work more efficiently.
+
+To use ClearML:
+
+!!! example ""
+
+    === "Python"
+        ```python
+        # pip install clearml
+        import clearml
+        
+        clearml.browser_login()
+        ```
+
+After running this script, you will need to sign in to your ClearML account on the browser and authenticate your session.
+
+### TensorBoard
+
+[TensorBoard](https://www.tensorflow.org/tensorboard) is a visualization toolkit for TensorFlow. It allows you to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.
+
+To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb):
+
+!!! example ""
+
+    === "CLI"
+        ```bash
+        load_ext tensorboard
+        tensorboard --logdir ultralytics/runs  # replace with 'runs' directory
+        ```
+
+To use TensorBoard locally run the below command and view results at http://localhost:6006/.
+
+!!! example ""
+
+    === "CLI"
+        ```bash
+        tensorboard --logdir ultralytics/runs  # replace with 'runs' directory
+        ```
+
+This will load TensorBoard and direct it to the directory where your training logs are saved.
+
+After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.

downloads/ultralytics-main/docs/modes/val.md

@@ -1,13 +1,12 @@
 ---
 comments: true
-description: Validate and improve YOLOv8n model accuracy on COCO128 and other datasets using hyperparameter & configuration tuning, in Val mode.
+description: 'Guide for Validating YOLOv8 Models: Learn how to evaluate the performance of your YOLO models using validation settings and metrics with Python and CLI examples.'
+keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperparameters, accuracy, metrics, Python, CLI
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
 
-**Val mode** is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a
-validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters
-of the model to improve its performance.
+**Val mode** is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
 
 !!! tip "Tip"
 
@@ -15,20 +14,19 @@ of the model to improve its performance.
 
 ## Usage Examples
 
-Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
-training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
+Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
 
 !!! example ""
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
-        
+
         # Load a model
         model = YOLO('yolov8n.pt')  # load an official model
         model = YOLO('path/to/best.pt')  # load a custom model
-        
+
         # Validate the model
         metrics = model.val()  # no arguments needed, dataset and settings remembered
         metrics.box.map    # map50-95
@@ -37,7 +35,7 @@ training `data` and arguments as model attributes. See Arguments section below f
         metrics.box.maps   # a list contains map50-95 of each category
         ```
     === "CLI"
-    
+
         ```bash
         yolo detect val model=yolov8n.pt  # val official model
         yolo detect val model=path/to/best.pt  # val custom model
@@ -45,18 +43,12 @@ training `data` and arguments as model attributes. See Arguments section below f
 
 ## Arguments
 
-Validation settings for YOLO models refer to the various hyperparameters and configurations used to
-evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and
-accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed
-during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation
-process include the size and composition of the validation dataset and the specific task the model is being used for. It
-is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
-validation dataset and to detect and prevent overfitting.
+Validation settings for YOLO models refer to the various hyperparameters and configurations used to evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation process include the size and composition of the validation dataset and the specific task the model is being used for. It is important to carefully tune and experiment with these settings to ensure that the model is performing well on the validation dataset and to detect and prevent overfitting.
 
 | Key           | Value   | Description                                                        |
 |---------------|---------|--------------------------------------------------------------------|
 | `data`        | `None`  | path to data file, i.e. coco128.yaml                               |
-| `imgsz`       | `640`   | image size as scalar or (h, w) list, i.e. (640, 480)               |
+| `imgsz`       | `640`   | size of input images as integer                                    |
 | `batch`       | `16`    | number of images per batch (-1 for AutoBatch)                      |
 | `save_json`   | `False` | save results to JSON file                                          |
 | `save_hybrid` | `False` | save hybrid version of labels (labels + additional predictions)    |
@@ -69,23 +61,4 @@ validation dataset and to detect and prevent overfitting.
 | `plots`       | `False` | show plots during training                                         |
 | `rect`        | `False` | rectangular val with each batch collated for minimum padding       |
 | `split`       | `val`   | dataset split to use for validation, i.e. 'val', 'test' or 'train' |
-
-## Export Formats
-
-Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument,
-i.e. `format='onnx'` or `format='engine'`.
-
-| Format                                                             | `format` Argument | Model                     | Metadata | Arguments                                           |
-|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|
-| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
-| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
-| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`                                     |
-| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
-| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlmodel`         | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
-| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`                                    |
-| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`                                             |
-| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`                             |
-| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`                                             |
-| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`                                             |
-| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`                                             |
+|

downloads/ultralytics-main/docs/overrides/partials/comments.html

@@ -11,7 +11,7 @@
         data-strict="0"
         data-reactions-enabled="1"
         data-emit-metadata="0"
-        data-input-position="bottom"
+        data-input-position="top"
         data-theme="preferred_color_scheme"
         data-lang="en"
         crossorigin="anonymous"

downloads/ultralytics-main/docs/quickstart.md

@@ -1,29 +1,85 @@
 ---
 comments: true
-description: Install and use YOLOv8 via CLI or Python. Run single-line commands or integrate with Python projects for object detection, segmentation, and classification.
+description: Explore various methods to install Ultralytics using pip, conda, git and Docker. Learn how to use Ultralytics with command line interface or within your Python projects.
+keywords: Ultralytics installation, pip install Ultralytics, Docker install Ultralytics, Ultralytics command line interface, Ultralytics Python interface
 ---
 
-## Install
+## Install Ultralytics
 
-Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning
-the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most
-up-to-date version.
+Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.
 
 !!! example "Install"
 
-    === "pip install (recommended)"
+    === "Pip install (recommended)"
+        Install the `ultralytics` package using pip, or update an existing installation by running `pip install -U ultralytics`. Visit the Python Package Index (PyPI) for more details on the `ultralytics` package: [https://pypi.org/project/ultralytics/](https://pypi.org/project/ultralytics/).
+
+        [![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
+
         ```bash
+        # Install the ultralytics package using pip
         pip install ultralytics
         ```
 
-    === "git clone (for development)"
+    === "Conda install"
+        Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at [https://anaconda.org/conda-forge/ultralytics](https://anaconda.org/conda-forge/ultralytics). Ultralytics feedstock repository for updating the conda package is at [https://github.com/conda-forge/ultralytics-feedstock/](https://github.com/conda-forge/ultralytics-feedstock/).
+
+
+        [![Conda Recipe](https://img.shields.io/badge/recipe-ultralytics-green.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics)
+
         ```bash
+        # Install the ultralytics package using conda
+        conda install -c conda-forge ultralytics
+        ```
+
+        !!! note
+        
+            If you are installing in a CUDA environment best practice is to install `ultralytics`, `pytorch` and `pytorch-cuda` in the same command to allow the conda package manager to resolve any conflicts, or else to install `pytorch-cuda` last to allow it override the CPU-specific `pytorch` package if necesary.
+            ```bash
+            # Install all packages together using conda
+            conda install -c conda-forge -c pytorch -c nvidia ultralytics pytorch torchvision pytorch-cuda=11.8 
+            ```
+
+    === "Git clone"
+        Clone the `ultralytics` repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode `-e` using pip.
+        ```bash
+        # Clone the ultralytics repository
         git clone https://github.com/ultralytics/ultralytics
+
+        # Navigate to the cloned directory
         cd ultralytics
+
+        # Install the package in editable mode for development
         pip install -e .
         ```
 
-See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that `pip` automatically installs all required dependencies.
+    === "Docker"
+        Utilize Docker to execute the `ultralytics` package in an isolated container. By employing the official `ultralytics` image from [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), you can avoid local installation. Below are the commands to get the latest image and execute it:
+
+        <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
+
+        ```bash
+        # Set image name as a variable
+        t=ultralytics/ultralytics:latest
+
+        # Pull the latest ultralytics image from Docker Hub
+        sudo docker pull $t
+
+        # Run the ultralytics image in a container with GPU support
+        sudo docker run -it --ipc=host --gpus all $t
+        ```
+
+        The above command initializes a Docker container with the latest `ultralytics` image. The `-it` flag assigns a pseudo-TTY and maintains stdin open, enabling you to interact with the container. The `--ipc=host` flag sets the IPC (Inter-Process Communication) namespace to the host, which is essential for sharing memory between processes. The `--gpus all` flag enables access to all available GPUs inside the container, which is crucial for tasks that require GPU computation.
+
+        Note: To work with files on your local machine within the container, use Docker volumes for mounting a local directory into the container:
+
+        ```bash
+        # Mount local directory to a directory inside the container
+        sudo docker run -it --ipc=host --gpus all -v /path/on/host:/path/in/container $t
+        ```
+
+        Alter `/path/on/host` with the directory path on your local machine, and `/path/in/container` with the desired path inside the Docker container for accessibility.
+
+See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that all examples above install all required dependencies.
 
 !!! tip "Tip"
 
@@ -33,9 +89,9 @@ See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralyt
         <img width="800" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
     </a>
 
-## Use with CLI
+## Use Ultralytics with CLI
 
-The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
+The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
 CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
 
 !!! example
@@ -102,7 +158,7 @@ CLI requires no customization or Python code. You can simply run all tasks from
 
 [CLI Guide](usage/cli.md){ .md-button .md-button--primary}
 
-## Use with Python
+## Use Ultralytics with Python
 
 YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement object detection, segmentation, and classification in their projects. This makes YOLOv8's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.
 
@@ -112,24 +168,111 @@ For example, users can load a model, train it, evaluate its performance on a val
 
     ```python
     from ultralytics import YOLO
-    
+
     # Create a new YOLO model from scratch
     model = YOLO('yolov8n.yaml')
-    
+
     # Load a pretrained YOLO model (recommended for training)
     model = YOLO('yolov8n.pt')
-    
+
     # Train the model using the 'coco128.yaml' dataset for 3 epochs
     results = model.train(data='coco128.yaml', epochs=3)
-    
+
     # Evaluate the model's performance on the validation set
     results = model.val()
-    
+
     # Perform object detection on an image using the model
     results = model('https://ultralytics.com/images/bus.jpg')
-    
+
     # Export the model to ONNX format
     success = model.export(format='onnx')
     ```
 
-[Python Guide](usage/python.md){.md-button .md-button--primary}
+[Python Guide](usage/python.md){.md-button .md-button--primary}
+
+## Ultralytics Settings
+
+The Ultralytics library provides a powerful settings management system to enable fine-grained control over your experiments. By making use of the `SettingsManager` housed within the `ultralytics.utils` module, users can readily access and alter their settings. These are stored in a YAML file and can be viewed or modified either directly within the Python environment or via the Command-Line Interface (CLI).
+
+### Inspecting Settings
+
+To gain insight into the current configuration of your settings, you can view them directly:
+
+!!! example "View settings"
+
+    === "Python"
+        You can use Python to view your settings. Start by importing the `settings` object from the `ultralytics` module. Print and return settings using the following commands:
+        ```python
+        from ultralytics import settings
+
+        # View all settings
+        print(settings)
+
+        # Return a specific setting
+        value = settings['runs_dir']
+        ```
+
+    === "CLI"
+        Alternatively, the command-line interface allows you to check your settings with a simple command:
+        ```bash
+        yolo settings
+        ```
+
+### Modifying Settings
+
+Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:
+
+!!! example "Update settings"
+
+    === "Python"
+        Within the Python environment, call the `update` method on the `settings` object to change your settings:
+        ```python
+        from ultralytics import settings
+
+        # Update a setting
+        settings.update({'runs_dir': '/path/to/runs'})
+
+        # Update multiple settings
+        settings.update({'runs_dir': '/path/to/runs', 'tensorboard': False})
+
+        # Reset settings to default values
+        settings.reset()
+        ```
+
+    === "CLI"
+        If you prefer using the command-line interface, the following commands will allow you to modify your settings:
+        ```bash
+        # Update a setting
+        yolo settings runs_dir='/path/to/runs'
+
+        # Update multiple settings
+        yolo settings runs_dir='/path/to/runs' tensorboard=False
+
+        # Reset settings to default values
+        yolo settings reset
+        ```
+
+### Understanding Settings
+
+The table below provides an overview of the settings available for adjustment within Ultralytics. Each setting is outlined along with an example value, the data type, and a brief description.
+
+| Name               | Example Value         | Data Type | Description                                                                                                      |
+|--------------------|-----------------------|-----------|------------------------------------------------------------------------------------------------------------------|
+| `settings_version` | `'0.0.4'`             | `str`     | Ultralytics _settings_ version (different from Ultralytics [pip](https://pypi.org/project/ultralytics/) version) |
+| `datasets_dir`     | `'/path/to/datasets'` | `str`     | The directory where the datasets are stored                                                                      |
+| `weights_dir`      | `'/path/to/weights'`  | `str`     | The directory where the model weights are stored                                                                 |
+| `runs_dir`         | `'/path/to/runs'`     | `str`     | The directory where the experiment runs are stored                                                               |
+| `uuid`             | `'a1b2c3d4'`          | `str`     | The unique identifier for the current settings                                                                   |
+| `sync`             | `True`                | `bool`    | Whether to sync analytics and crashes to HUB                                                                     |
+| `api_key`          | `''`                  | `str`     | Ultralytics HUB [API Key](https://hub.ultralytics.com/settings?tab=api+keys)                                     |
+| `clearml`          | `True`                | `bool`    | Whether to use ClearML logging                                                                                   |
+| `comet`            | `True`                | `bool`    | Whether to use [Comet ML](https://bit.ly/yolov8-readme-comet) for experiment tracking and visualization          |
+| `dvc`              | `True`                | `bool`    | Whether to use [DVC for experiment tracking](https://dvc.org/doc/dvclive/ml-frameworks/yolo) and version control |
+| `hub`              | `True`                | `bool`    | Whether to use [Ultralytics HUB](https://hub.ultralytics.com) integration                                        |
+| `mlflow`           | `True`                | `bool`    | Whether to use MLFlow for experiment tracking                                                                    |
+| `neptune`          | `True`                | `bool`    | Whether to use Neptune for experiment tracking                                                                   |
+| `raytune`          | `True`                | `bool`    | Whether to use Ray Tune for hyperparameter tuning                                                                |
+| `tensorboard`      | `True`                | `bool`    | Whether to use TensorBoard for visualization                                                                     |
+| `wandb`            | `True`                | `bool`    | Whether to use Weights & Biases logging                                                                          |
+
+As you navigate through your projects or experiments, be sure to revisit these settings to ensure that they are optimally configured for your needs.

downloads/ultralytics-main/docs/reference/cfg/__init__.md

@@ -0,0 +1,54 @@
+---
+description: Explore Ultralytics cfg functions like cfg2dict, handle_deprecation, merge_equal_args & more to handle YOLO settings and configurations efficiently.
+keywords: Ultralytics, YOLO, Configuration, cfg2dict, handle_deprecation, merge_equals_args, handle_yolo_settings, copy_default_cfg, Image Detection
+---
+
+# Reference for `ultralytics/cfg/__init__.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/__init__.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/__init__.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.cfg.cfg2dict
+<br><br>
+
+---
+## ::: ultralytics.cfg.get_cfg
+<br><br>
+
+---
+## ::: ultralytics.cfg._handle_deprecation
+<br><br>
+
+---
+## ::: ultralytics.cfg.check_dict_alignment
+<br><br>
+
+---
+## ::: ultralytics.cfg.merge_equals_args
+<br><br>
+
+---
+## ::: ultralytics.cfg.handle_yolo_hub
+<br><br>
+
+---
+## ::: ultralytics.cfg.handle_yolo_settings
+<br><br>
+
+---
+## ::: ultralytics.cfg.parse_key_value_pair
+<br><br>
+
+---
+## ::: ultralytics.cfg.smart_value
+<br><br>
+
+---
+## ::: ultralytics.cfg.entrypoint
+<br><br>
+
+---
+## ::: ultralytics.cfg.copy_default_cfg
+<br><br>

downloads/ultralytics-main/docs/reference/data/annotator.md

@@ -0,0 +1,14 @@
+---
+description: Enhance your machine learning model with Ultralytics’ auto_annotate function. Simplify data annotation for improved model training.
+keywords: Ultralytics, Auto-Annotate, Machine Learning, AI, Annotation, Data Processing, Model Training
+---
+
+# Reference for `ultralytics/data/annotator.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/annotator.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.annotator.auto_annotate
+<br><br>

downloads/ultralytics-main/docs/reference/data/augment.md

@@ -0,0 +1,86 @@
+---
+description: Detailed exploration into Ultralytics data augmentation methods including BaseTransform, MixUp, LetterBox, ToTensor, and more for enhancing model performance.
+keywords: Ultralytics, Data Augmentation, BaseTransform, MixUp, RandomHSV, LetterBox, Albumentations, classify_transforms, classify_albumentations
+---
+
+# Reference for `ultralytics/data/augment.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.augment.BaseTransform
+<br><br>
+
+---
+## ::: ultralytics.data.augment.Compose
+<br><br>
+
+---
+## ::: ultralytics.data.augment.BaseMixTransform
+<br><br>
+
+---
+## ::: ultralytics.data.augment.Mosaic
+<br><br>
+
+---
+## ::: ultralytics.data.augment.MixUp
+<br><br>
+
+---
+## ::: ultralytics.data.augment.RandomPerspective
+<br><br>
+
+---
+## ::: ultralytics.data.augment.RandomHSV
+<br><br>
+
+---
+## ::: ultralytics.data.augment.RandomFlip
+<br><br>
+
+---
+## ::: ultralytics.data.augment.LetterBox
+<br><br>
+
+---
+## ::: ultralytics.data.augment.CopyPaste
+<br><br>
+
+---
+## ::: ultralytics.data.augment.Albumentations
+<br><br>
+
+---
+## ::: ultralytics.data.augment.Format
+<br><br>
+
+---
+## ::: ultralytics.data.augment.ClassifyLetterBox
+<br><br>
+
+---
+## ::: ultralytics.data.augment.CenterCrop
+<br><br>
+
+---
+## ::: ultralytics.data.augment.ToTensor
+<br><br>
+
+---
+## ::: ultralytics.data.augment.v8_transforms
+<br><br>
+
+---
+## ::: ultralytics.data.augment.classify_transforms
+<br><br>
+
+---
+## ::: ultralytics.data.augment.hsv2colorjitter
+<br><br>
+
+---
+## ::: ultralytics.data.augment.classify_albumentations
+<br><br>

downloads/ultralytics-main/docs/reference/data/base.md

@@ -0,0 +1,14 @@
+---
+description: Explore BaseDataset in Ultralytics docs. Learn how this implementation simplifies dataset creation and manipulation.
+keywords: Ultralytics, docs, BaseDataset, data manipulation, dataset creation
+---
+
+# Reference for `ultralytics/data/base.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.base.BaseDataset
+<br><br>

downloads/ultralytics-main/docs/reference/data/build.md

@@ -0,0 +1,38 @@
+---
+description: Explore the Ultralytics YOLO v3 data build procedures, including the InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source.
+keywords: Ultralytics, YOLO v3, Data build, DataLoader, InfiniteDataLoader, seed_worker, build_dataloader, load_inference_source
+---
+
+# Reference for `ultralytics/data/build.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/build.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.build.InfiniteDataLoader
+<br><br>
+
+---
+## ::: ultralytics.data.build._RepeatSampler
+<br><br>
+
+---
+## ::: ultralytics.data.build.seed_worker
+<br><br>
+
+---
+## ::: ultralytics.data.build.build_yolo_dataset
+<br><br>
+
+---
+## ::: ultralytics.data.build.build_dataloader
+<br><br>
+
+---
+## ::: ultralytics.data.build.check_source
+<br><br>
+
+---
+## ::: ultralytics.data.build.load_inference_source
+<br><br>

downloads/ultralytics-main/docs/reference/data/converter.md

@@ -0,0 +1,38 @@
+---
+description: Explore Ultralytics data converter functions like coco91_to_coco80_class, merge_multi_segment, rle2polygon for efficient data handling.
+keywords: Ultralytics, Data Converter, coco91_to_coco80_class, merge_multi_segment, rle2polygon
+---
+
+# Reference for `ultralytics/data/converter.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.converter.coco91_to_coco80_class
+<br><br>
+
+---
+## ::: ultralytics.data.converter.coco80_to_coco91_class
+<br><br>
+
+---
+## ::: ultralytics.data.converter.convert_coco
+<br><br>
+
+---
+## ::: ultralytics.data.converter.convert_dota_to_yolo_obb
+<br><br>
+
+---
+## ::: ultralytics.data.converter.rle2polygon
+<br><br>
+
+---
+## ::: ultralytics.data.converter.min_index
+<br><br>
+
+---
+## ::: ultralytics.data.converter.merge_multi_segment
+<br><br>

downloads/ultralytics-main/docs/reference/data/dataset.md

@@ -0,0 +1,22 @@
+---
+description: Explore the YOLODataset and SemanticDataset classes in YOLO data. Learn how to efficiently handle and manipulate your data with Ultralytics.
+keywords: Ultralytics, YOLO, YOLODataset, SemanticDataset, data handling, data manipulation
+---
+
+# Reference for `ultralytics/data/dataset.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/dataset.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/dataset.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.dataset.YOLODataset
+<br><br>
+
+---
+## ::: ultralytics.data.dataset.ClassificationDataset
+<br><br>
+
+---
+## ::: ultralytics.data.dataset.SemanticDataset
+<br><br>

downloads/ultralytics-main/docs/reference/data/loaders.md

@@ -0,0 +1,42 @@
+---
+description: Find detailed guides on Ultralytics YOLO data loaders, including LoadStreams, LoadImages and LoadTensor. Learn how to get the best YouTube URLs.
+keywords: Ultralytics, data loaders, LoadStreams, LoadImages, LoadTensor, YOLO, YouTube URLs
+---
+
+# Reference for `ultralytics/data/loaders.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/loaders.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/loaders.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.loaders.SourceTypes
+<br><br>
+
+---
+## ::: ultralytics.data.loaders.LoadStreams
+<br><br>
+
+---
+## ::: ultralytics.data.loaders.LoadScreenshots
+<br><br>
+
+---
+## ::: ultralytics.data.loaders.LoadImages
+<br><br>
+
+---
+## ::: ultralytics.data.loaders.LoadPilAndNumpy
+<br><br>
+
+---
+## ::: ultralytics.data.loaders.LoadTensor
+<br><br>
+
+---
+## ::: ultralytics.data.loaders.autocast_list
+<br><br>
+
+---
+## ::: ultralytics.data.loaders.get_best_youtube_url
+<br><br>

downloads/ultralytics-main/docs/reference/data/utils.md

@@ -0,0 +1,58 @@
+---
+description: Uncover a detailed guide to Ultralytics data utilities. Learn functions from img2label_paths to autosplit, all boosting your YOLO model’s efficiency.
+keywords: Ultralytics, data utils, YOLO, img2label_paths, exif_size, polygon2mask, polygons2masks_overlap, check_cls_dataset, delete_dsstore, autosplit
+---
+
+# Reference for `ultralytics/data/utils.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.data.utils.HUBDatasetStats
+<br><br>
+
+---
+## ::: ultralytics.data.utils.img2label_paths
+<br><br>
+
+---
+## ::: ultralytics.data.utils.get_hash
+<br><br>
+
+---
+## ::: ultralytics.data.utils.exif_size
+<br><br>
+
+---
+## ::: ultralytics.data.utils.verify_image_label
+<br><br>
+
+---
+## ::: ultralytics.data.utils.polygon2mask
+<br><br>
+
+---
+## ::: ultralytics.data.utils.polygons2masks
+<br><br>
+
+---
+## ::: ultralytics.data.utils.polygons2masks_overlap
+<br><br>
+
+---
+## ::: ultralytics.data.utils.check_det_dataset
+<br><br>
+
+---
+## ::: ultralytics.data.utils.check_cls_dataset
+<br><br>
+
+---
+## ::: ultralytics.data.utils.compress_one_image
+<br><br>
+
+---
+## ::: ultralytics.data.utils.autosplit
+<br><br>

downloads/ultralytics-main/docs/reference/engine/exporter.md

@@ -0,0 +1,30 @@
+---
+description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, IOSDetectModel, and try exporting with examples.
+keywords: Ultralytics, Exporter, IOSDetectModel, Export Formats, Try export
+---
+
+# Reference for `ultralytics/engine/exporter.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/exporter.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/exporter.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.engine.exporter.Exporter
+<br><br>
+
+---
+## ::: ultralytics.engine.exporter.IOSDetectModel
+<br><br>
+
+---
+## ::: ultralytics.engine.exporter.export_formats
+<br><br>
+
+---
+## ::: ultralytics.engine.exporter.gd_outputs
+<br><br>
+
+---
+## ::: ultralytics.engine.exporter.try_export
+<br><br>

downloads/ultralytics-main/docs/reference/engine/model.md

@@ -0,0 +1,14 @@
+---
+description: Explore the detailed guide on using the Ultralytics YOLO Engine Model. Learn better ways to implement, train and evaluate YOLO models.
+keywords: Ultralytics, YOLO, engine model, documentation, guide, implementation, training, evaluation
+---
+
+# Reference for `ultralytics/engine/model.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.engine.model.Model
+<br><br>

downloads/ultralytics-main/docs/reference/engine/predictor.md

@@ -0,0 +1,14 @@
+---
+description: Learn about Ultralytics BasePredictor, an essential component of our engine that serves as the foundation for all prediction operations.
+keywords: Ultralytics, BasePredictor, YOLO, prediction, engine
+---
+
+# Reference for `ultralytics/engine/predictor.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/predictor.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/predictor.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.engine.predictor.BasePredictor
+<br><br>

downloads/ultralytics-main/docs/reference/engine/results.md

@@ -0,0 +1,34 @@
+---
+description: Master Ultralytics engine results including base tensors, boxes, and keypoints with our thorough documentation.
+keywords: Ultralytics, engine, results, base tensor, boxes, keypoints
+---
+
+# Reference for `ultralytics/engine/results.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/results.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/results.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.engine.results.BaseTensor
+<br><br>
+
+---
+## ::: ultralytics.engine.results.Results
+<br><br>
+
+---
+## ::: ultralytics.engine.results.Boxes
+<br><br>
+
+---
+## ::: ultralytics.engine.results.Masks
+<br><br>
+
+---
+## ::: ultralytics.engine.results.Keypoints
+<br><br>
+
+---
+## ::: ultralytics.engine.results.Probs
+<br><br>

downloads/ultralytics-main/docs/reference/engine/trainer.md

@@ -0,0 +1,14 @@
+---
+description: Learn about the BaseTrainer class in the Ultralytics library. From training control, customization to advanced usage.
+keywords: Ultralytics, BaseTrainer, Machine Learning, Training Control, Python library
+---
+
+# Reference for `ultralytics/engine/trainer.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/trainer.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/trainer.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.engine.trainer.BaseTrainer
+<br><br>

downloads/ultralytics-main/docs/reference/engine/validator.md

@@ -0,0 +1,14 @@
+---
+description: Learn about the Ultralytics BaseValidator module. Understand its principles, uses, and how it interacts with other components.
+keywords: Ultralytics, BaseValidator, Ultralytics engine, module, components
+---
+
+# Reference for `ultralytics/engine/validator.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/validator.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/engine/validator.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.engine.validator.BaseValidator
+<br><br>

downloads/ultralytics-main/docs/reference/hub/__init__.md

@@ -0,0 +1,38 @@
+---
+description: Explore Ultralytics hub functions for model resetting, checking datasets, model exporting and more. Easy-to-follow instructions provided.
+keywords: Ultralytics, hub functions, model export, dataset check, reset model, YOLO Docs
+---
+
+# Reference for `ultralytics/hub/__init__.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/__init__.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/__init__.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.hub.login
+<br><br>
+
+---
+## ::: ultralytics.hub.logout
+<br><br>
+
+---
+## ::: ultralytics.hub.reset_model
+<br><br>
+
+---
+## ::: ultralytics.hub.export_fmts_hub
+<br><br>
+
+---
+## ::: ultralytics.hub.export_model
+<br><br>
+
+---
+## ::: ultralytics.hub.get_export
+<br><br>
+
+---
+## ::: ultralytics.hub.check_dataset
+<br><br>

downloads/ultralytics-main/docs/reference/hub/auth.md

@@ -1,8 +1,14 @@
 ---
-description: Learn how to use Ultralytics hub authentication in your projects with examples and guidelines from the Auth page on Ultralytics Docs.
+description: Dive into the Ultralytics Auth API documentation & learn how to manage authentication in your AI & ML projects easily and effectively.
+keywords: Ultralytics, Auth, API documentation, User Authentication, AI, Machine Learning
 ---
 
-# Auth
+# Reference for `ultralytics/hub/auth.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/auth.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/auth.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
 ---
-:::ultralytics.hub.auth.Auth
+## ::: ultralytics.hub.auth.Auth
 <br><br>

downloads/ultralytics-main/docs/reference/hub/session.md

@@ -1,8 +1,14 @@
 ---
-description: Accelerate your AI development with the Ultralytics HUB Training Session. High-performance training of object detection models.
+description: Explore details about the HUBTrainingSession in Ultralytics framework. Learn to utilize this functionality for effective model training.
+keywords: Ultralytics, HUBTrainingSession, Documentation, Model Training, AI, Machine Learning, YOLO
 ---
 
-# HUBTrainingSession
+# Reference for `ultralytics/hub/session.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/session.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/session.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
 ---
-:::ultralytics.hub.session.HUBTrainingSession
+## ::: ultralytics.hub.session.HUBTrainingSession
 <br><br>

downloads/ultralytics-main/docs/reference/hub/utils.md

@@ -1,23 +1,26 @@
 ---
-description: Explore Ultralytics events, including 'request_with_credentials' and 'smart_request', to improve your project's performance and efficiency.
+description: Explore Ultralytics docs for various Events, including "request_with_credentials" and "requests_with_progress". Also, understand the use of the "smart_request".
+keywords: Ultralytics, Events, request_with_credentials, smart_request, Ultralytics hub utils, requests_with_progress
 ---
 
-# Events
+# Reference for `ultralytics/hub/utils.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/hub/utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
 ---
-:::ultralytics.hub.utils.Events
+## ::: ultralytics.hub.utils.Events
 <br><br>
 
-# request_with_credentials
 ---
-:::ultralytics.hub.utils.request_with_credentials
+## ::: ultralytics.hub.utils.request_with_credentials
 <br><br>
 
-# requests_with_progress
 ---
-:::ultralytics.hub.utils.requests_with_progress
+## ::: ultralytics.hub.utils.requests_with_progress
 <br><br>
 
-# smart_request
 ---
-:::ultralytics.hub.utils.smart_request
+## ::: ultralytics.hub.utils.smart_request
 <br><br>

downloads/ultralytics-main/docs/reference/models/fastsam/model.md

@@ -0,0 +1,14 @@
+---
+description: Learn all about Ultralytics FastSAM model. Dive into our comprehensive guide for seamless integration and efficient model training.
+keywords: Ultralytics, FastSAM model, Model documentation, Efficient model training
+---
+
+# Reference for `ultralytics/models/fastsam/model.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.fastsam.model.FastSAM
+<br><br>

downloads/ultralytics-main/docs/reference/models/fastsam/predict.md

@@ -0,0 +1,14 @@
+---
+description: Get detailed insights about Ultralytics FastSAMPredictor. Learn to predict and optimize your AI models with our properly documented guidelines.
+keywords: Ultralytics, FastSAMPredictor, predictive modeling, AI optimization, machine learning, deep learning, Ultralytics documentation
+---
+
+# Reference for `ultralytics/models/fastsam/predict.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.fastsam.predict.FastSAMPredictor
+<br><br>

downloads/ultralytics-main/docs/reference/models/fastsam/prompt.md

@@ -0,0 +1,14 @@
+---
+description: Learn to effectively utilize FastSAMPrompt model from Ultralytics. Detailed guide to help you get the most out of your machine learning models.
+keywords: Ultralytics, FastSAMPrompt, machine learning, model, guide, documentation
+---
+
+# Reference for `ultralytics/models/fastsam/prompt.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/prompt.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/prompt.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.fastsam.prompt.FastSAMPrompt
+<br><br>

downloads/ultralytics-main/docs/reference/models/fastsam/utils.md

@@ -0,0 +1,18 @@
+---
+description: Learn how to adjust bounding boxes to image borders in Ultralytics models using the bbox_iou utility. Enhance your object detection performance.
+keywords: Ultralytics, bounding boxes, Bboxes, image borders, object detection, bbox_iou, model utilities
+---
+
+# Reference for `ultralytics/models/fastsam/utils.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/utils.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.fastsam.utils.adjust_bboxes_to_image_border
+<br><br>
+
+---
+## ::: ultralytics.models.fastsam.utils.bbox_iou
+<br><br>

downloads/ultralytics-main/docs/reference/models/fastsam/val.md

@@ -0,0 +1,14 @@
+---
+description: Learn about FastSAMValidator in Ultralytics models. Comprehensive guide to enhancing AI capabilities with Ultralytics.
+keywords: Ultralytics, FastSAMValidator, model, synthetic, AI, machine learning, validation
+---
+
+# Reference for `ultralytics/models/fastsam/val.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.fastsam.val.FastSAMValidator
+<br><br>

downloads/ultralytics-main/docs/reference/models/nas/model.md

@@ -0,0 +1,14 @@
+---
+description: Learn how our NAS model operates in Ultralytics. Comprehensive guide with detailed examples. Master the nuances of Ultralytics NAS model.
+keywords: Ultralytics, NAS model, NAS guide, machine learning, model documentation
+---
+
+# Reference for `ultralytics/models/nas/model.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.nas.model.NAS
+<br><br>

downloads/ultralytics-main/docs/reference/models/nas/predict.md

@@ -0,0 +1,14 @@
+---
+description: Explore Ultralytics NASPredictor. Understand high-level architecture of the model for effective implementation and efficient predictions.
+keywords: NASPredictor, Ultralytics, Ultralytics model, model architecture, efficient predictions
+---
+
+# Reference for `ultralytics/models/nas/predict.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.nas.predict.NASPredictor
+<br><br>

downloads/ultralytics-main/docs/reference/models/nas/val.md

@@ -0,0 +1,14 @@
+---
+description: Explore the utilities and functions of the Ultralytics NASValidator. Find out how it benefits allocation and optimization in AI models.
+keywords: Ultralytics, NASValidator, models.nas.val.NASValidator, AI models, allocation, optimization
+---
+
+# Reference for `ultralytics/models/nas/val.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.nas.val.NASValidator
+<br><br>

downloads/ultralytics-main/docs/reference/models/rtdetr/model.md

@@ -0,0 +1,14 @@
+---
+description: Explore the specifics of using the RTDETR model in Ultralytics. Detailed documentation layered with explanations and examples.
+keywords: Ultralytics, RTDETR model, Ultralytics models, object detection, Ultralytics documentation
+---
+
+# Reference for `ultralytics/models/rtdetr/model.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.rtdetr.model.RTDETR
+<br><br>

downloads/ultralytics-main/docs/reference/models/rtdetr/predict.md

@@ -0,0 +1,14 @@
+---
+description: Learn how to use the RTDETRPredictor model of the Ultralytics package. Detailed documentation, usage instructions, and advice.
+keywords: Ultralytics, RTDETRPredictor, model documentation, guide, real-time object detection
+---
+
+# Reference for `ultralytics/models/rtdetr/predict.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/predict.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/predict.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.rtdetr.predict.RTDETRPredictor
+<br><br>

downloads/ultralytics-main/docs/reference/models/rtdetr/train.md

@@ -0,0 +1,14 @@
+---
+description: Get insights into RTDETRTrainer, a crucial component of Ultralytics for effective model training. Explore detailed documentation at Ultralytics.
+keywords: Ultralytics, RTDETRTrainer, model training, Ultralytics models, PyTorch models, neural networks, machine learning, deep learning
+---
+
+# Reference for `ultralytics/models/rtdetr/train.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/train.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/train.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.rtdetr.train.RTDETRTrainer
+<br><br>

downloads/ultralytics-main/docs/reference/models/rtdetr/val.md

@@ -0,0 +1,18 @@
+---
+description: Explore RTDETRDataset in Ultralytics Models. Learn about the RTDETRValidator function, understand its usage in real-time object detection.
+keywords: Ultralytics, RTDETRDataset, RTDETRValidator, real-time object detection, models documentation
+---
+
+# Reference for `ultralytics/models/rtdetr/val.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/val.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/val.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.rtdetr.val.RTDETRDataset
+<br><br>
+
+---
+## ::: ultralytics.models.rtdetr.val.RTDETRValidator
+<br><br>

downloads/ultralytics-main/docs/reference/models/sam/amg.md

@@ -0,0 +1,54 @@
+---
+description: Explore Ultralytics methods for mask data processing, transformation and encoding. Deepen your understanding of RLE encoding, image cropping and more.
+keywords: Ultralytics, Mask Data, Transformation, Encoding, RLE encoding, Image cropping, Pytorch, SAM, AMG, Ultralytics model
+---
+
+# Reference for `ultralytics/models/sam/amg.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/amg.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/amg.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.sam.amg.is_box_near_crop_edge
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.batch_iterator
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.calculate_stability_score
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.build_point_grid
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.build_all_layer_point_grids
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.generate_crop_boxes
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.uncrop_boxes_xyxy
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.uncrop_points
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.uncrop_masks
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.remove_small_regions
+<br><br>
+
+---
+## ::: ultralytics.models.sam.amg.batched_mask_to_box
+<br><br>

downloads/ultralytics-main/docs/reference/models/sam/build.md

@@ -0,0 +1,34 @@
+---
+description: Master building SAM ViT models with Ultralytics. Discover steps to leverage the power of SAM and Vision Transformer sessions.
+keywords: Ultralytics, SAM, build sam, vision transformer, vits, build_sam_vit_l, build_sam_vit_b, build_sam
+---
+
+# Reference for `ultralytics/models/sam/build.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/build.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/build.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.sam.build.build_sam_vit_h
+<br><br>
+
+---
+## ::: ultralytics.models.sam.build.build_sam_vit_l
+<br><br>
+
+---
+## ::: ultralytics.models.sam.build.build_sam_vit_b
+<br><br>
+
+---
+## ::: ultralytics.models.sam.build.build_mobile_sam
+<br><br>
+
+---
+## ::: ultralytics.models.sam.build._build_sam
+<br><br>
+
+---
+## ::: ultralytics.models.sam.build.build_sam
+<br><br>

downloads/ultralytics-main/docs/reference/models/sam/model.md

@@ -0,0 +1,14 @@
+---
+description: Dive into the SAM model details in the Ultralytics YOLO documentation. Understand, implement, and optimize your model use.
+keywords: Ultralytics, YOLO, SAM Model, Documentations, Machine Learning, AI, Convolutional neural network
+---
+
+# Reference for `ultralytics/models/sam/model.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/model.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/model.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.sam.model.SAM
+<br><br>

downloads/ultralytics-main/docs/reference/models/sam/modules/decoders.md

@@ -0,0 +1,18 @@
+---
+description: Explore MaskDecoder, a part of the Ultralytics models. Gain insights on how to utilize it effectively in the SAM modules decoders MLP.
+keywords: Ultralytics, MaskDecoder, SAM modules, decoders, MLP, YOLO, machine learning, image recognition
+---
+
+# Reference for `ultralytics/models/sam/modules/decoders.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/decoders.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/decoders.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.sam.modules.decoders.MaskDecoder
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.decoders.MLP
+<br><br>

downloads/ultralytics-main/docs/reference/models/sam/modules/encoders.md

@@ -0,0 +1,50 @@
+---
+description: Discover detailed information on ImageEncoderViT, PositionEmbeddingRandom, Attention, window_partition, get_rel_pos and more in Ultralytics models encoders documentation.
+keywords: Ultralytics, Encoders, Modules, Documentation, ImageEncoderViT, PositionEmbeddingRandom, Attention, window_partition, get_rel_pos
+---
+
+# Reference for `ultralytics/models/sam/modules/encoders.py`
+
+!!! note
+
+    Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/encoders.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/modules/encoders.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
+
+---
+## ::: ultralytics.models.sam.modules.encoders.ImageEncoderViT
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.PromptEncoder
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.PositionEmbeddingRandom
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.Block
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.Attention
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.PatchEmbed
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.window_partition
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.window_unpartition
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.get_rel_pos
+<br><br>
+
+---
+## ::: ultralytics.models.sam.modules.encoders.add_decomposed_rel_pos
+<br><br>