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@@ -37,3 +37,4 @@ ultralytics/assets/bus.jpg filter=lfs diff=lfs merge=lfs -text
 ultralytics/data/__pycache__/augment.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
 ultralytics/data/__pycache__/augment.cpython-38.pyc filter=lfs diff=lfs merge=lfs -text
 ultralytics/data/__pycache__/augment.cpython-39.pyc filter=lfs diff=lfs merge=lfs -text
+ultralytics-main/ultralytics/assets/bus.jpg filter=lfs diff=lfs merge=lfs -text

ultralytics-main/.dockerignore

@@ -0,0 +1,35 @@
+# Python
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.py[cod]
+*$py.class
+.pytest_cache
+.coverage
+coverage.xml
+.ruff_cache
+*.egg-info
+dist
+build
+
+# Development
+.env
+.venv
+env/
+venv/
+ENV/
+.idea
+.vscode
+*.swp
+*.swo
+.DS_Store
+
+# Project specific
+*.log
+benchmarks.log
+runs/
+
+# Dependencies
+node_modules/

ultralytics-main/.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,98 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+name: 🐛 Bug Report
+# title: " "
+description: Problems with Ultralytics YOLO
+labels: [bug, triage]
+type: "bug"
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting an Ultralytics YOLO 🐛 Bug Report!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the Ultralytics [Docs](https://docs.ultralytics.com/) and [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar bug report already exists.
+      options:
+        - label: >
+            I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
+          required: true
+
+  - type: dropdown
+    attributes:
+      label: Ultralytics YOLO Component
+      description: |
+        Please select the Ultralytics YOLO component where you found the bug.
+      multiple: true
+      options:
+        - "Install"
+        - "Train"
+        - "Val"
+        - "Predict"
+        - "Export"
+        - "Multi-GPU"
+        - "Augmentation"
+        - "Hyperparameter Tuning"
+        - "Integrations"
+        - "Other"
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Bug
+      description: Please provide as much information as possible. Copy and paste console output and error messages including the _full_ traceback. Use [Markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to format text, code and logs. If necessary, include screenshots for visual elements only. Providing detailed information will help us resolve the issue more efficiently.
+      placeholder: |
+        💡 ProTip! Include as much information as possible (logs, tracebacks, screenshots, etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: Try the latest version (`pip install -U ultralytics`) before reporting a bug. If it's still present, please provide the output of `yolo checks` (CLI) or `ultralytics.utils.checks.collect_system_info()` (Python) command to help us diagnose the problem.
+      placeholder: |
+        Paste output of `yolo checks` (CLI) or `ultralytics.utils.checks.collect_system_info()` (Python) command, i.e.:
+        ```
+        Ultralytics 8.3.2 🚀 Python-3.11.2 torch-2.4.1 CPU (Apple M3)
+        Setup complete ✅ (8 CPUs, 16.0 GB RAM, 266.5/460.4 GB disk)
+
+        OS                  macOS-13.5.2
+        Environment         Jupyter
+        Python              3.11.2
+        Install             git
+        RAM                 16.00 GB
+        CPU                 Apple M3
+        CUDA                None
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Minimal Reproducible Example
+      description: >
+        When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimal reproducible example](https://docs.ultralytics.com/help/minimum-reproducible-example/).
+      placeholder: |
+        ```
+        # Code to reproduce your issue here
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/ultralytics/pulls) (PR) to help improve Ultralytics YOLO for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the Ultralytics YOLO [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started.
+      options:
+        - label: Yes I'd like to help by submitting a PR!

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

@@ -0,0 +1,16 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+blank_issues_enabled: true
+contact_links:
+  - name: 📄 Docs
+    url: https://docs.ultralytics.com/
+    about: Full Ultralytics YOLO Documentation
+  - name: 💬 Forum
+    url: https://community.ultralytics.com/
+    about: Ask on Ultralytics Community Forum
+  - name: 🎧 Discord
+    url: https://ultralytics.com/discord
+    about: Ask on Ultralytics Discord
+  - name: ⌨️ Reddit
+    url: https://reddit.com/r/ultralytics
+    about: Ask on Ultralytics Subreddit

ultralytics-main/.github/ISSUE_TEMPLATE/feature-request.yml

@@ -0,0 +1,53 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+name: 🚀 Feature Request
+description: Suggest an Ultralytics YOLO idea
+# title: " "
+labels: [enhancement]
+type: "feature"
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting an Ultralytics 🚀 Feature Request!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the Ultralytics [Docs](https://docs.ultralytics.com/) and [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar feature request already exists.
+      options:
+        - label: >
+            I have searched the Ultralytics [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar feature requests.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Description
+      description: A short description of your feature.
+      placeholder: |
+        What new feature would you like to see in YOLO?
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Use case
+      description: |
+        Describe the use case of your feature request. It will help us understand and prioritize the feature request.
+      placeholder: |
+        How would this feature be used, and who would use it?
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/ultralytics/pulls) (PR) to help improve YOLO for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started.
+      options:
+        - label: Yes I'd like to help by submitting a PR!

ultralytics-main/.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+name: ❓ Question
+description: Ask an Ultralytics YOLO question
+# title: " "
+labels: [question]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for asking an Ultralytics YOLO ❓ Question!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the Ultralytics [Docs](https://docs.ultralytics.com/), [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) to see if a similar question already exists.
+      options:
+        - label: >
+            I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Question
+      description: What is your question? Please provide as much information as possible. Include detailed code examples to reproduce the problem and describe the context in which the issue occurs. Format your text and code using [Markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) for clarity and readability. Following these guidelines will help us assist you more effectively.
+      placeholder: |
+        💡 ProTip! Include as much information as possible (logs, tracebacks, screenshots etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?

ultralytics-main/.github/dependabot.yml

@@ -0,0 +1,24 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Dependabot for package version updates
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: pip
+    directory: "/"
+    schedule:
+      interval: daily
+      time: "04:00"
+    open-pull-requests-limit: 10
+    labels:
+      - dependencies
+
+  - package-ecosystem: github-actions
+    directory: "/.github/workflows"
+    schedule:
+      interval: daily
+      time: "04:00"
+    open-pull-requests-limit: 5
+    labels:
+      - dependencies

ultralytics-main/.github/workflows/ci.yml

@@ -0,0 +1,523 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO Continuous Integration (CI) GitHub Actions tests
+
+name: CI
+
+permissions:
+  contents: read
+
+env:
+  PYTHONFAULTHANDLER: 1
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  schedule:
+    - cron: "0 8 * * *" # runs at 08:00 UTC every day
+  workflow_dispatch:
+    inputs:
+      hub:
+        description: "Run HUB"
+        default: true
+        type: boolean
+      benchmarks:
+        description: "Run Benchmarks"
+        default: true
+        type: boolean
+      tests:
+        description: "Run Tests"
+        default: true
+        type: boolean
+      gpu:
+        description: "Run GPU"
+        default: true
+        type: boolean
+      raspberrypi:
+        description: "Run Raspberry Pi"
+        default: true
+        type: boolean
+      nvidia-jetson:
+        description: "Run NVIDIA Jetson"
+        default: true
+        type: boolean
+      conda:
+        description: "Run Conda"
+        default: true
+        type: boolean
+
+jobs:
+  HUB:
+    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: ["3.12"]
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.python }}
+      - uses: astral-sh/setup-uv@v7
+      - name: Install requirements
+        shell: bash # for Windows compatibility
+        run: |
+          uv pip install --system . --extra-index-url https://download.pytorch.org/whl/cpu
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Test HUB training
+        shell: python
+        env:
+          API_KEY: ${{ secrets.ULTRALYTICS_HUB_API_KEY }}
+          MODEL_ID: ${{ secrets.ULTRALYTICS_HUB_MODEL_ID }}
+        run: |
+          import os
+          from ultralytics import YOLO, hub
+          api_key, model_id = os.environ['API_KEY'], os.environ['MODEL_ID']
+          hub.login(api_key)
+          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
+      matrix:
+        # Temporarily disable windows-latest due to https://github.com/ultralytics/ultralytics/actions/runs/13020330819/job/36319338854?pr=18921
+        os: [ubuntu-latest, macos-26, ubuntu-24.04-arm]
+        python: ["3.12"]
+        model: [yolo11n]
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.python }}
+      - uses: astral-sh/setup-uv@v7
+      - name: Install requirements
+        shell: bash # for Windows compatibility
+        run: |
+          uv pip install --system -e ".[export]" "coverage[toml]" --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Benchmark DetectionModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.309
+      - name: Benchmark ClassificationModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.249
+      - name: Benchmark YOLOWorld DetectionModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/yolov8s-worldv2.pt' imgsz=160 verbose=0.337
+      - name: Benchmark SegmentationModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.195
+      - name: Benchmark PoseModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.197
+      - name: Benchmark OBBModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-obb.pt' imgsz=160 verbose=0.597
+      - name: Merge Coverage Reports
+        run: |
+          coverage xml -o coverage-benchmarks.xml
+      - name: Upload Coverage Reports to CodeCov
+        if: github.repository == 'ultralytics/ultralytics'
+        uses: codecov/codecov-action@v5
+        with:
+          flags: Benchmarks
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+      - name: Prune uv Cache
+        run: uv cache prune --ci
+      - name: Benchmark Summary
+        run: |
+          cat benchmarks.log
+          echo '```' >> $GITHUB_STEP_SUMMARY
+          cat benchmarks.log >> $GITHUB_STEP_SUMMARY
+          echo '```' >> $GITHUB_STEP_SUMMARY
+
+  Tests:
+    if: github.event_name == 'pull_request' || github.event_name == 'push'
+    timeout-minutes: 60
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-26, windows-latest, ubuntu-24.04-arm]
+        python: ["3.12"]
+        torch: [latest]
+        include:
+          - os: ubuntu-latest
+            python: "3.8" # torch 1.8.0 requires python >=3.6, <=3.9
+            torch: "1.8.0"
+            torchvision: "0.9.0"
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.python }}
+      - uses: astral-sh/setup-uv@v7
+      - name: Install requirements
+        shell: bash # for Windows compatibility
+        run: |
+          slow=""
+          torch=""
+          if [ "${{ matrix.torch }}" != "latest" ]; then
+              torch="torch==${{ matrix.torch }} torchvision==${{ matrix.torchvision }}"
+          fi
+          uv pip install --system -e ".[export,solutions]" $torch pytest-cov --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Pytest tests
+        shell: bash # for Windows compatibility
+        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 == '3.12'
+        uses: codecov/codecov-action@v5
+        with:
+          flags: Tests
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+      - name: Prune uv Cache
+        run: uv cache prune --ci
+
+  SlowTests:
+    if: (github.event_name == 'workflow_dispatch' && github.event.inputs.tests == 'true') || github.event_name == 'schedule'
+    timeout-minutes: 360
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-26, windows-latest, ubuntu-24.04-arm]
+        python: ["3.12"]
+        torch: [latest]
+        include:
+          - os: ubuntu-latest
+            python: "3.8" # torch 1.8.0 requires python >=3.6, <=3.9
+            torch: "1.8.0"
+            torchvision: "0.9.0"
+          - os: ubuntu-latest
+            python: "3.9"
+            torch: "1.9.0"
+            torchvision: "0.10.0"
+          - os: ubuntu-latest
+            python: "3.9"
+            torch: "1.10.0"
+            torchvision: "0.11.0"
+          - os: ubuntu-latest
+            python: "3.10"
+            torch: "1.11.0"
+            torchvision: "0.12.0"
+          - os: ubuntu-latest
+            python: "3.10"
+            torch: "1.12.0"
+            torchvision: "0.13.0"
+          - os: ubuntu-latest
+            python: "3.10"
+            torch: "1.13.0"
+            torchvision: "0.14.0"
+          - os: ubuntu-latest
+            python: "3.11"
+            torch: "2.0.0"
+            torchvision: "0.15.0"
+          - os: ubuntu-latest
+            python: "3.11"
+            torch: "2.1.0"
+            torchvision: "0.16.0"
+          - os: ubuntu-latest
+            python: "3.12"
+            torch: "2.2.0"
+            torchvision: "0.17.0"
+          - os: ubuntu-latest
+            python: "3.12"
+            torch: "2.3.0"
+            torchvision: "0.18.0"
+          - os: ubuntu-latest
+            python: "3.12"
+            torch: "2.4.0"
+            torchvision: "0.19.0"
+          - os: ubuntu-latest
+            python: "3.12"
+            torch: "2.5.0"
+            torchvision: "0.20.0"
+          - os: ubuntu-latest
+            python: "3.12"
+            torch: "2.6.0"
+            torchvision: "0.21.0"
+          - os: ubuntu-latest
+            python: "3.12"
+            torch: "2.7.0"
+            torchvision: "0.22.0"
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.python }}
+      - uses: astral-sh/setup-uv@v7
+      - name: Install requirements
+        shell: bash # for Windows compatibility
+        run: |
+          torch=""
+          if [ "${{ matrix.torch }}" != "latest" ]; then
+              torch="torch==${{ matrix.torch }} torchvision==${{ matrix.torchvision }}"
+          fi
+          uv pip install --system -e ".[export,solutions]" $torch faster-coco-eval mlflow pytest-cov --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Pytest tests
+        uses: ultralytics/actions/retry@main
+        with:
+          shell: bash # for Windows compatibility
+          run: pytest --slow --cov=ultralytics/ --cov-report=xml tests/
+          retries: 1 # Retry once after initial attempt (2 total runs)
+          retry_delay_seconds: 60
+      - name: Prune uv Cache
+        run: uv cache prune --ci
+
+  GPU:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name != 'workflow_dispatch' || github.event.inputs.gpu == 'true')
+    timeout-minutes: 60
+    runs-on: gpu-latest
+    steps:
+      - uses: actions/checkout@v6
+      - uses: astral-sh/setup-uv@v7
+        with:
+          activate-environment: true
+      - name: Install requirements
+        run: |
+          uv pip install -e . pytest-cov nvidia-ml-py
+        env:
+          PIP_BREAK_SYSTEM_PACKAGES: 1
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Pytest tests
+        run: |
+          slow=""
+          if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
+            slow="--slow"
+          fi
+          pytest $slow --cov=ultralytics/ --cov-report xml tests/test_cuda.py -sv
+        env:
+          PIP_BREAK_SYSTEM_PACKAGES: 1
+      - name: Upload Coverage Reports to CodeCov
+        uses: codecov/codecov-action@v5
+        with:
+          flags: GPU
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+
+  RaspberryPi:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.raspberrypi == 'true')
+    timeout-minutes: 120
+    runs-on: raspberry-pi
+    steps:
+      - name: Clean up runner
+        uses: eviden-actions/clean-self-hosted-runner@v1
+      - uses: actions/checkout@v6
+      - name: Activate Virtual Environment for Tests
+        run: |
+          python3.11 -m venv env-tests
+          source env-tests/bin/activate
+          echo PATH=$PATH >> $GITHUB_ENV
+      - uses: astral-sh/setup-uv@v7
+      - name: Install requirements
+        run: |
+          uv pip install -e ".[export]" pytest mlflow faster-coco-eval --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Pytest tests
+        run: pytest --slow tests/
+      - name: Activate Virtual Environment for Benchmarks
+        run: |
+          python3.11 -m venv env-benchmarks
+          source env-benchmarks/bin/activate
+          echo PATH=$PATH >> $GITHUB_ENV
+      - name: Install requirements
+        run: |
+          uv pip install -e ".[export]" pytest mlflow faster-coco-eval --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Benchmark DetectionModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolo11n.pt' imgsz=160 verbose=0.309
+      - name: Benchmark ClassificationModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolo11n-cls.pt' imgsz=160 verbose=0.249
+      - name: Benchmark YOLOWorld DetectionModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolov8s-worldv2.pt' imgsz=160 verbose=0.337
+      - name: Benchmark SegmentationModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolo11n-seg.pt' imgsz=160 verbose=0.195
+      - name: Benchmark PoseModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolo11n-pose.pt' imgsz=160 verbose=0.197
+      - name: Benchmark OBBModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolo11n-obb.pt' imgsz=160 verbose=0.597
+      - name: Benchmark Summary
+        run: |
+          cat benchmarks.log
+          echo "$(cat benchmarks.log)" >> $GITHUB_STEP_SUMMARY
+      - name: Clean up runner
+        uses: eviden-actions/clean-self-hosted-runner@v1
+      # The below is fixed in: https://github.com/ultralytics/ultralytics/pull/15987
+      # - name: Reboot # run a reboot command in the background to free resources for next run and not crash main thread
+      #   run: sudo bash -c "sleep 10; reboot" &
+
+  NVIDIA_Jetson:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.nvidia-jetson == 'true')
+    timeout-minutes: 120
+    runs-on: ${{ matrix.runner }}
+    strategy:
+      fail-fast: false
+      matrix:
+        name: [JetPack6.2, JetPack5.1.2]
+        include:
+          - name: JetPack6.2
+            python: "3.10"
+            runner: jetson-jp62
+            numpy: "1.26.4"
+            torch_whl: "https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.5.0a0+872d972e41.nv24.08-cp310-cp310-linux_aarch64.whl"
+            torchvision_whl: "https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.20.0a0+afc54f7-cp310-cp310-linux_aarch64.whl"
+            onnxruntime_whl: "https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.20.0-cp310-cp310-linux_aarch64.whl"
+          - name: JetPack5.1.2
+            python: "3.8"
+            runner: jetson-jp512
+            numpy: "1.23.5"
+            torch_whl: "https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.2.0-cp38-cp38-linux_aarch64.whl"
+            torchvision_whl: "https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.17.2+c1d70fe-cp38-cp38-linux_aarch64.whl"
+            onnxruntime_whl: "https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.16.3-cp38-cp38-linux_aarch64.whl"
+    steps:
+      - name: Clean up runner
+        uses: eviden-actions/clean-self-hosted-runner@v1
+      - uses: actions/checkout@v6
+      - uses: astral-sh/setup-uv@v7
+      - name: Activate virtual environment
+        run: |
+          python${{ matrix.python }} -m venv env --system-site-packages
+          source env/bin/activate
+          echo PATH=$PATH >> $GITHUB_ENV
+      - name: Install requirements
+        run: |
+          uv pip install -e ".[export]" pytest \
+          "${{ matrix.torch_whl }}" "${{ matrix.torchvision_whl }}" "${{ matrix.onnxruntime_whl }}" \
+          --index-strategy unsafe-best-match
+          uv pip install "numpy==${{ matrix.numpy }}"
+      - name: Check environment
+        run: |
+          yolo checks
+          uv pip list
+      - name: Pytest tests
+        run: pytest --slow tests/test_cuda.py
+      - name: Clean up runner
+        uses: eviden-actions/clean-self-hosted-runner@v1
+
+  Conda:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.conda == 'true')
+    timeout-minutes: 120
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python: ["3.12"]
+    defaults:
+      run:
+        shell: bash -el {0}
+    steps:
+      - uses: astral-sh/setup-uv@v7
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          python-version: ${{ matrix.python }}
+          channels: conda-forge,defaults
+          channel-priority: true
+          activate-environment: anaconda-client-env
+      - name: Install Ultralytics package from conda-forge
+        run: conda install -c pytorch -c conda-forge pytorch-cpu torchvision ultralytics openvino
+      - name: Install pip packages
+        run: uv pip install pytest
+      - name: Check environment
+        run: conda list
+      - name: Test CLI
+        run: |
+          yolo predict model=yolo11n.pt imgsz=320
+          yolo train model=yolo11n.pt data=coco8.yaml epochs=1 imgsz=32
+          yolo val model=yolo11n.pt data=coco8.yaml imgsz=32
+          yolo export model=yolo11n.pt format=torchscript imgsz=160
+          yolo benchmark model=yolo11n.pt data='coco8.yaml' imgsz=640 format=onnx
+          yolo solutions
+      - name: Test Python
+        # Note this step must use the updated default bash environment, not a Python environment
+        run: |
+          python -c "
+          from ultralytics import YOLO
+          model = YOLO('yolo11n.pt')
+          results = model.train(data='coco8.yaml', epochs=3, imgsz=160)
+          results = model.val(imgsz=160)
+          results = model.predict(imgsz=160)
+          results = model.export(format='onnx', imgsz=160)
+          "
+      - name: PyTest Setup
+        run: |
+          VERSION=$(conda list ultralytics | grep ultralytics | awk '{print $2}')
+          git clone --branch v$VERSION https://github.com/ultralytics/ultralytics.git
+      - name: test_cli.py
+        run: pytest ultralytics/tests/test_cli.py -v -s
+      - name: test_cuda.py
+        run: pytest ultralytics/tests/test_cuda.py -v -s
+      - name: test_engine.py
+        run: pytest ultralytics/tests/test_engine.py -v -s
+      - name: test_exports.py
+        run: pytest ultralytics/tests/test_exports.py -v -s
+      - name: test_integrations.py
+        run: pytest ultralytics/tests/test_integrations.py -v -s
+      - name: test_solutions.py
+        run: pytest ultralytics/tests/test_solutions.py -v -s
+      # WARNING: tests hang here for unknown reasons https://github.com/ultralytics/ultralytics/pull/21577
+      # - name: test_python.py
+      #   run: pytest ultralytics/tests/test_python.py -vv -s
+
+  Summary:
+    runs-on: ubuntu-latest
+    needs: [HUB, Benchmarks, Tests, GPU, RaspberryPi, NVIDIA_Jetson, Conda]
+    if: always()
+    steps:
+      - name: Check for failure and notify
+        if: (needs.HUB.result == 'failure' || needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure' || needs.GPU.result == 'failure' || needs.RaspberryPi.result == 'failure' || needs.NVIDIA_Jetson.result == 'failure' || needs.Conda.result == 'failure' ) && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push') && github.run_attempt == '1'
+        uses: slackapi/[email protected]
+        with:
+          webhook-type: incoming-webhook
+          webhook: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
+          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"

ultralytics-main/.github/workflows/cla.yml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA
+# This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged
+
+name: CLA Assistant
+on:
+  issue_comment:
+    types:
+      - created
+  pull_request_target:
+    types:
+      - reopened
+      - opened
+      - synchronize
+
+permissions:
+  actions: write
+  contents: write
+  pull-requests: write
+  statuses: write
+
+jobs:
+  CLA:
+    if: github.repository == 'ultralytics/ultralytics'
+    runs-on: ubuntu-latest
+    steps:
+      - name: CLA Assistant
+        if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'
+        uses: contributor-assistant/[email protected]
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          # Must be repository secret PAT
+          PERSONAL_ACCESS_TOKEN: ${{ secrets._GITHUB_TOKEN }}
+        with:
+          path-to-signatures: "signatures/version1/cla.json"
+          path-to-document: "https://docs.ultralytics.com/help/CLA" # CLA document
+          # Branch must not be protected
+          branch: cla-signatures
+          allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot*
+
+          remote-organization-name: ultralytics
+          remote-repository-name: cla
+          custom-pr-sign-comment: "I have read the CLA Document and I sign the CLA"
+          custom-allsigned-prcomment: All Contributors have signed the CLA. ✅

ultralytics-main/.github/workflows/docker.yml

@@ -0,0 +1,284 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest images on DockerHub https://hub.docker.com/r/ultralytics
+
+name: Publish Docker Images
+
+permissions:
+  contents: read
+
+on:
+  push:
+    branches: [main]
+    paths-ignore:
+      - "docs/**"
+      - "mkdocs.yml"
+  workflow_dispatch:
+    inputs:
+      Dockerfile:
+        type: boolean
+        description: Dockerfile (+ runner, export)
+        default: true
+      Dockerfile-python:
+        type: boolean
+        description: Dockerfile-python (+ jupyter, cpu, python-export)
+        default: true
+      Dockerfile-arm64:
+        type: boolean
+        description: Dockerfile-arm64
+        default: true
+      Dockerfile-jetson-jetpack6:
+        type: boolean
+        description: Dockerfile-jetson-jetpack6
+        default: true
+      Dockerfile-jetson-jetpack5:
+        type: boolean
+        description: Dockerfile-jetson-jetpack5
+        default: true
+      Dockerfile-jetson-jetpack4:
+        type: boolean
+        description: Dockerfile-jetson-jetpack4
+        default: true
+      Dockerfile-conda:
+        type: boolean
+        description: Dockerfile-conda
+        default: true
+      push:
+        type: boolean
+        description: Publish to DockerHub and ghcr.io
+
+jobs:
+  docker:
+    if: github.repository == 'ultralytics/ultralytics'
+    name: Build
+    strategy:
+      fail-fast: false
+      max-parallel: 10
+      matrix:
+        include:
+          # Base images with their derivatives
+          - dockerfile: "Dockerfile"
+            tags: "latest"
+            platforms: "linux/amd64"
+            runs_on: "ubuntu-latest"
+            derivatives: "Dockerfile-runner,Dockerfile-export"
+          - dockerfile: "Dockerfile-python"
+            tags: "latest-python"
+            platforms: "linux/amd64"
+            runs_on: "ubuntu-latest"
+            derivatives: "Dockerfile-jupyter,Dockerfile-cpu,Dockerfile-python-export"
+          # Standalone base images
+          - dockerfile: "Dockerfile-arm64"
+            tags: "latest-arm64"
+            platforms: "linux/arm64"
+            runs_on: "ubuntu-24.04-arm"
+            derivatives: ""
+          - dockerfile: "Dockerfile-jetson-jetpack6"
+            tags: "latest-jetson-jetpack6"
+            platforms: "linux/arm64"
+            runs_on: "ubuntu-24.04-arm"
+            derivatives: ""
+          - dockerfile: "Dockerfile-jetson-jetpack5"
+            tags: "latest-jetson-jetpack5"
+            platforms: "linux/arm64"
+            runs_on: "ubuntu-24.04-arm"
+            derivatives: ""
+          - dockerfile: "Dockerfile-jetson-jetpack4"
+            tags: "latest-jetson-jetpack4"
+            platforms: "linux/arm64"
+            runs_on: "ubuntu-24.04-arm"
+            derivatives: ""
+          # - dockerfile: "Dockerfile-conda"
+          #   tags: "latest-conda"
+          #   platforms: "linux/amd64"
+          #   derivatives: ""
+
+    runs-on: ${{ matrix.runs_on }}
+    outputs:
+      new_release: ${{ steps.check_tag.outputs.new_release }}
+    steps:
+      - name: Cleanup disk space
+        uses: ultralytics/actions/cleanup-disk@main
+
+      - name: Checkout repo
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0 # copy full .git directory to access full git history in Docker images
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Login to GHCR
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets._GITHUB_TOKEN }}
+
+      - name: Login to NVIDIA NGC
+        uses: docker/login-action@v3
+        with:
+          registry: nvcr.io
+          username: $oauthtoken
+          password: ${{ secrets.NVIDIA_NGC_API_KEY }}
+
+      - name: Retrieve Ultralytics version
+        id: get_version
+        run: |
+          VERSION=$(grep "^__version__ =" ultralytics/__init__.py | awk -F'"' '{print $2}')
+          echo "Retrieved Ultralytics version: $VERSION"
+          echo "version=$VERSION" >> $GITHUB_OUTPUT
+          VERSION_TAG=$(echo "${{ matrix.tags }}" | sed "s/latest/${VERSION}/")
+          echo "Intended version tag: $VERSION_TAG"
+          echo "version_tag=$VERSION_TAG" >> $GITHUB_OUTPUT
+
+      - name: Check if version tag exists on DockerHub
+        id: check_tag
+        run: |
+          RESPONSE=$(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 "new_release=false" >> $GITHUB_OUTPUT
+          elif [[ "$MESSAGE" == *"404"* ]]; then
+              echo "Tag $VERSION_TAG does not exist on DockerHub."
+              echo "new_release=true" >> $GITHUB_OUTPUT
+          else
+              echo "Unexpected response from DockerHub. Please check manually."
+              echo "new_release=false" >> $GITHUB_OUTPUT
+          fi
+        env:
+          VERSION_TAG: ${{ steps.get_version.outputs.version_tag }}
+
+      - name: Build Base Image
+        if: github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true'
+        uses: ultralytics/actions/retry@main
+        with:
+          timeout_minutes: 120
+          retry_delay_seconds: 60
+          retries: 2
+          run: |
+            docker build \
+              --platform ${{ matrix.platforms }} \
+              --label "org.opencontainers.image.source=https://github.com/ultralytics/ultralytics" \
+              --label "org.opencontainers.image.description=Ultralytics image" \
+              --label "org.opencontainers.image.licenses=AGPL-3.0-or-later" \
+              -f docker/${{ matrix.dockerfile }} \
+              -t ultralytics/ultralytics:${{ matrix.tags }} \
+              -t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} \
+              -t ghcr.io/ultralytics/ultralytics:${{ matrix.tags }} \
+              -t ghcr.io/ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} \
+              .
+
+      - name: Build Derivative Images
+        if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.derivatives != ''
+        uses: ultralytics/actions/retry@main
+        with:
+          timeout_minutes: 120
+          retry_delay_seconds: 60
+          retries: 2
+          run: |
+            # Build each derivative image using local base image
+            derivatives='${{ matrix.derivatives }}'
+            if [[ -n "$derivatives" ]]; then
+              IFS=',' read -ra derivative_array <<< "$derivatives"
+              for derivative in "${derivative_array[@]}"; do
+                # Determine derivative tags
+                derivative_tag=$(echo "$derivative" | sed 's/Dockerfile-/latest-/')
+                derivative_version_tag=$(echo "$derivative_tag" | sed "s/latest/${{ steps.get_version.outputs.version }}/")
+
+                echo "Building $derivative -> $derivative_tag"
+                docker build \
+                  --platform ${{ matrix.platforms }} \
+                  --label "org.opencontainers.image.source=https://github.com/ultralytics/ultralytics" \
+                  --label "org.opencontainers.image.description=Ultralytics $derivative image" \
+                  --label "org.opencontainers.image.licenses=AGPL-3.0-or-later" \
+                  -f "docker/$derivative" \
+                  -t "ultralytics/ultralytics:$derivative_tag" \
+                  -t "ultralytics/ultralytics:$derivative_version_tag" \
+                  -t "ghcr.io/ultralytics/ultralytics:$derivative_tag" \
+                  -t "ghcr.io/ultralytics/ultralytics:$derivative_version_tag" \
+                  .
+              done
+            fi
+
+      - name: Check Environment
+        if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && (matrix.platforms == 'linux/amd64' || matrix.platforms == 'linux/arm64') && matrix.dockerfile != 'Dockerfile-conda'
+        run: docker run ultralytics/ultralytics:${{ (matrix.tags == 'latest-python' && 'latest-python-export') || (matrix.tags == 'latest' && 'latest-export') || matrix.tags }} /bin/bash -c "yolo checks && uv pip list"
+
+      - name: Run Tests
+        if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && (matrix.platforms == 'linux/amd64' || matrix.platforms == 'linux/arm64') && matrix.dockerfile != 'Dockerfile-conda'
+        run: docker run ultralytics/ultralytics:${{ (matrix.tags == 'latest-python' && 'latest-python-export') || (matrix.tags == 'latest' && 'latest-export') || matrix.tags }} /bin/bash -c "uv pip install --system --break-system-packages pytest && pytest tests"
+
+      - name: Run Benchmarks
+        if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && (matrix.platforms == 'linux/amd64' || matrix.dockerfile == 'Dockerfile-arm64') && matrix.dockerfile != 'Dockerfile' && matrix.dockerfile != 'Dockerfile-conda'
+        run: docker run ultralytics/ultralytics:${{ (matrix.tags == 'latest-python' && 'latest-python-export') || (matrix.tags == 'latest' && 'latest-export') || matrix.tags }} yolo benchmark model=yolo11n.pt imgsz=160 verbose=0.309
+
+      - name: Push All Images
+        if: github.event_name == 'push' || (github.event.inputs[matrix.dockerfile] == 'true' && github.event.inputs.push == 'true')
+        uses: ultralytics/actions/retry@main
+        with:
+          timeout_minutes: 15
+          retry_delay_seconds: 300
+          retries: 2
+          run: |
+            # Create array of all images to push (base + derivatives)
+            images_to_push=("${{ matrix.tags }}")
+
+            # Add derivative images to array
+            derivatives='${{ matrix.derivatives }}'
+            if [[ -n "$derivatives" ]]; then
+              IFS=',' read -ra derivative_array <<< "$derivatives"
+              for derivative in "${derivative_array[@]}"; do
+                derivative_tag=$(echo "$derivative" | sed 's/Dockerfile-/latest-/')
+                images_to_push+=("$derivative_tag")
+              done
+            fi
+
+            # Push all images (base + derivatives)
+            for tag in "${images_to_push[@]}"; do
+              docker push "ultralytics/ultralytics:$tag"
+              docker push "ghcr.io/ultralytics/ultralytics:$tag"
+
+              # Push version tag if new release
+              if [[ "${{ steps.check_tag.outputs.new_release }}" == "true" && "${{ matrix.dockerfile }}" != "Dockerfile-conda" ]]; then
+                version_tag=$(echo "$tag" | sed "s/latest/${{ steps.get_version.outputs.version }}/")
+                docker push "ultralytics/ultralytics:$version_tag"
+                docker push "ghcr.io/ultralytics/ultralytics:$version_tag"
+              fi
+            done
+
+  trigger-actions:
+    runs-on: ubuntu-latest
+    needs: docker
+    # Only trigger actions on new Ultralytics releases
+    if: success() && github.repository == 'ultralytics/ultralytics' && github.event_name == 'push' && needs.docker.outputs.new_release == 'true'
+    steps:
+      - name: Trigger Additional GitHub Actions
+        env:
+          GH_TOKEN: ${{ secrets._GITHUB_TOKEN }}
+        run: |
+          sleep 60
+          gh workflow run deploy_cloud_run.yml \
+            --repo ultralytics/assistant \
+            --ref main
+
+  notify:
+    runs-on: ubuntu-latest
+    needs: [docker, trigger-actions]
+    if: always()
+    steps:
+      - name: Check for failure and notify
+        if: needs.docker.result == 'failure' && github.repository == 'ultralytics/ultralytics' && github.event_name == 'push' && github.run_attempt == '1'
+        uses: slackapi/[email protected]
+        with:
+          webhook-type: incoming-webhook
+          webhook: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
+          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"

ultralytics-main/.github/workflows/docs.yml

@@ -0,0 +1,125 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Test and publish docs to https://docs.ultralytics.com
+# Ignores the following Docs rules to match Google-style docstrings:
+# D100: Missing docstring in public module
+# D104: Missing docstring in public package
+# D203: 1 blank line required before class docstring
+# D205: 1 blank line required between summary line and description
+# D212: Multi-line docstring summary should start at the first line
+# D213: Multi-line docstring summary should start at the second line
+# D401: First line of docstring should be in imperative mood
+# D406: Section name should end with a newline
+# D407: Missing dashed underline after section
+# D413: Missing blank line after last section
+
+name: Publish Docs
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  workflow_dispatch:
+    inputs:
+      publish_docs:
+        description: "Publish live to https://docs.ultralytics.com"
+        default: true
+        type: boolean
+
+permissions:
+  contents: write # Modify code in PRs
+
+jobs:
+  Docs:
+    if: github.repository == 'ultralytics/ultralytics'
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_REF: ${{ github.head_ref || github.ref }}
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v6
+        with:
+          # Fetch depth 0 required to capture full docs author history
+          repository: ${{ github.event.pull_request.head.repo.full_name || github.repository }}
+          token: ${{ secrets._GITHUB_TOKEN || secrets.GITHUB_TOKEN }}
+          ref: ${{ env.GITHUB_REF }}
+          fetch-depth: 0
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.x"
+      - uses: astral-sh/setup-uv@v7
+      - name: Install Dependencies
+        run: uv pip install --system -e ".[dev]" ruff black --extra-index-url https://download.pytorch.org/whl/cpu
+      - name: Ruff fixes
+        continue-on-error: true
+        run: |
+          ruff check \
+          --fix \
+          --unsafe-fixes \
+          --extend-select F,I,D,UP,RUF,FA \
+          --target-version py39 \
+          --ignore D100,D104,D203,D205,D212,D213,D401,D406,D407,D413,RUF001,RUF002,RUF012 \
+          .
+      - name: Update Docs Reference Section and Push Changes
+        continue-on-error: true
+        run: |
+          git config --global user.name "UltralyticsAssistant"
+          git config --global user.email "[email protected]"
+          npm install --global prettier prettier-plugin-sh
+          python docs/build_reference.py
+          git pull origin "$GITHUB_REF"
+          git add .
+          git reset HEAD -- .github/workflows/  # workflow changes are not permitted with default token
+          if [[ "${{ github.event_name }}" == "pull_request" ]] && ! git diff --staged --quiet; then
+            git commit -m "Auto-update Ultralytics Docs Reference by https://ultralytics.com/actions"
+            git push
+          else
+            echo "No changes to commit"
+          fi
+      - name: Ruff checks
+        run: |
+          ruff check \
+          --extend-select F,I,D,UP,RUF,FA \
+          --target-version py39 \
+          --ignore D100,D104,D203,D205,D212,D213,D401,D406,D407,D413,RUF001,RUF002,RUF012 \
+          .
+      - name: Build Docs and Check for Warnings
+        run: |
+          python docs/build_docs.py
+      - name: Commit and Push Docs changes
+        continue-on-error: true
+        if: always()
+        run: |
+          git pull origin "$GITHUB_REF"
+          git add --update  # only add updated files
+          git reset HEAD -- .github/workflows/  # workflow changes are not permitted with default token
+          if [[ "${{ github.event_name }}" == "pull_request" ]] && ! git diff --staged --quiet; then
+            git commit -m "Auto-update Ultralytics Docs by https://ultralytics.com/actions"
+            git push
+          else
+            echo "No changes to commit"
+          fi
+      - name: Publish Docs to https://docs.ultralytics.com
+        if: github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && github.event.inputs.publish_docs == 'true')
+        run: |
+          git clone --depth 1 --branch gh-pages https://github.com/ultralytics/docs.git docs-repo
+          cd docs-repo
+          if [ -f "vercel.json" ]; then
+            cp vercel.json /tmp/vercel.json
+          fi
+          rm -rf *
+          cp -R ../site/* .
+          if [ -f "/tmp/vercel.json" ]; then
+            cp /tmp/vercel.json .
+          fi
+          echo "${{ secrets.INDEXNOW_KEY_DOCS }}" > "${{ secrets.INDEXNOW_KEY_DOCS }}.txt"
+          git add .
+          if git diff --staged --quiet; then
+            echo "No changes to commit"
+          else
+            git pull origin gh-pages
+            LATEST_HASH=$(git rev-parse --short=7 HEAD)
+            git commit -m "Update Docs for 'ultralytics ${{ steps.check_pypi.outputs.version }} - $LATEST_HASH'"
+            git push https://${{ secrets._GITHUB_TOKEN }}@github.com/ultralytics/docs.git gh-pages
+          fi

ultralytics-main/.github/workflows/format.yml

@@ -0,0 +1,68 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics Actions https://github.com/ultralytics/actions
+# This workflow formats code and documentation in PRs to Ultralytics standards
+
+name: Ultralytics Actions
+
+on:
+  issues:
+    types: [opened, edited]
+  discussion:
+    types: [created]
+  pull_request:
+    types: [opened, closed, synchronize, review_requested]
+
+permissions:
+  contents: write # Modify code in PRs
+  pull-requests: write # Add comments and labels to PRs
+  issues: write # Add comments and labels to issues
+
+jobs:
+  actions:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Run Ultralytics Actions
+        uses: ultralytics/actions@main
+        with:
+          token: ${{ secrets._GITHUB_TOKEN || secrets.GITHUB_TOKEN }} # Auto-generated token
+          labels: true # Auto-label issues/PRs using AI
+          python: true # Format Python with Ruff and docformatter
+          prettier: true # Format YAML, JSON, Markdown, CSS
+          swift: false # Format Swift (requires macos-latest)
+          spelling: true # Check spelling with codespell
+          links: false # Check broken links with Lychee
+          summary: true # Generate AI-powered PR summaries
+          openai_api_key: ${{ secrets.OPENAI_API_KEY }} # Powers PR summaries, labels and comments
+          brave_api_key: ${{ secrets.BRAVE_API_KEY }} # Used for broken link resolution
+          first_issue_response: |
+            👋 Hello @${{ github.actor }}, thank you for your interest in Ultralytics 🚀! We recommend a visit to the [Docs](https://docs.ultralytics.com/) for new users where you can find many [Python](https://docs.ultralytics.com/usage/python/) and [CLI](https://docs.ultralytics.com/usage/cli/) usage examples and where many of the most common questions may already be answered.
+
+            If this is a 🐛 Bug Report, please provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum-reproducible-example/) to help us debug it.
+
+            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/guides/model-training-tips/).
+
+            Join the Ultralytics community where it suits you best. For real-time chat, head to [Discord](https://discord.com/invite/ultralytics) 🎧. Prefer in-depth discussions? Check out [Discourse](https://community.ultralytics.com/). Or dive into threads on our [Subreddit](https://www.reddit.com/r/Ultralytics/) to share knowledge with the community.
+
+            ## Upgrade
+
+            Upgrade to the latest `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) to verify your issue is not already resolved in the latest version:
+
+            ```bash
+            pip install -U ultralytics
+            ```
+
+            ## Environments
+
+            YOLO may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
+
+            - **Notebooks** with free GPU: <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolo11"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
+            - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
+            - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <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>
+
+            ## Status
+
+            <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml?query=event%3Aschedule"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
+
+            If this badge is green, all [Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml?query=event%3Aschedule) tests are currently passing. CI tests verify correct operation of all YOLO [Modes](https://docs.ultralytics.com/modes/) and [Tasks](https://docs.ultralytics.com/tasks/) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

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

@@ -0,0 +1,104 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee
+# Ignores the following status codes to reduce false positives:
+#   - 401(Vimeo, 'unauthorized')
+#   - 403(OpenVINO, 'forbidden')
+#   - 429(Instagram, 'too many requests')
+#   - 500(Zenodo, 'cached')
+#   - 502(Zenodo, 'bad gateway')
+#   - 999(LinkedIn, 'unknown status code')
+
+name: Check Broken links
+
+permissions:
+  contents: read
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0 * * *" # runs at 00:00 UTC every day
+
+jobs:
+  Links:
+    if: github.repository == 'ultralytics/ultralytics'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Install lychee
+        run: curl -sSfL "https://github.com/lycheeverse/lychee/releases/latest/download/lychee-x86_64-unknown-linux-gnu.tar.gz" | sudo tar xz -C /usr/local/bin
+
+      - name: Test Markdown and HTML links with retry
+        uses: ultralytics/actions/retry@main
+        with:
+          timeout_minutes: 60
+          retry_delay_seconds: 1800
+          retries: 2
+          run: |
+            lychee \
+            --scheme https \
+            --timeout 60 \
+            --insecure \
+            --accept 100..=103,200..=299,401,403,429,500,502,999 \
+            --exclude-all-private \
+            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
+            --exclude-path docs/zh \
+            --exclude-path docs/es \
+            --exclude-path docs/ru \
+            --exclude-path docs/pt \
+            --exclude-path docs/fr \
+            --exclude-path docs/de \
+            --exclude-path docs/ja \
+            --exclude-path docs/ko \
+            --exclude-path docs/hi \
+            --exclude-path docs/ar \
+            --github-token ${{ secrets.GITHUB_TOKEN }} \
+            --header "User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36" \
+            './**/*.md' \
+            './**/*.html' | tee -a $GITHUB_STEP_SUMMARY
+
+            # Raise error if broken links found
+            if ! grep -q "0 Errors" $GITHUB_STEP_SUMMARY; then
+              exit 1
+            fi
+
+      - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
+        if: github.event_name == 'workflow_dispatch'
+        uses: ultralytics/actions/retry@main
+        with:
+          timeout_minutes: 60
+          retry_delay_seconds: 1800
+          retries: 2
+          run: |
+            lychee \
+            --scheme https \
+            --timeout 60 \
+            --insecure \
+            --accept 100..=103,200..=299,401,403,429,500,502,999 \
+            --exclude-all-private \
+            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
+            --exclude-path './**/ci.yml' \
+            --exclude-path docs/zh \
+            --exclude-path docs/es \
+            --exclude-path docs/ru \
+            --exclude-path docs/pt \
+            --exclude-path docs/fr \
+            --exclude-path docs/de \
+            --exclude-path docs/ja \
+            --exclude-path docs/ko \
+            --exclude-path docs/hi \
+            --exclude-path docs/ar \
+            --github-token ${{ secrets.GITHUB_TOKEN }} \
+            --header "User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36" \
+            './**/*.md' \
+            './**/*.html' \
+            './**/*.yml' \
+            './**/*.yaml' \
+            './**/*.py' \
+            './**/*.ipynb' | tee -a $GITHUB_STEP_SUMMARY
+
+            # Raise error if broken links found
+            if ! grep -q "0 Errors" $GITHUB_STEP_SUMMARY; then
+              exit 1
+            fi

ultralytics-main/.github/workflows/merge-main-into-prs.yml

@@ -0,0 +1,91 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Automatically merges repository 'main' branch into all open PRs to keep them up-to-date
+# Action runs on updates to main branch so when one PR merges to main all others update
+
+name: Merge main into PRs
+
+on:
+  workflow_dispatch:
+  # push:
+  #   branches:
+  #     - ${{ github.event.repository.default_branch }}
+
+permissions:
+  contents: write # Modify code in PRs
+
+jobs:
+  Merge:
+    if: github.repository == 'ultralytics/ultralytics'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.x"
+          cache: "pip"
+      - name: Install requirements
+        run: |
+          pip install pygithub
+      - name: Merge default branch into PRs
+        shell: python
+        run: |
+          from github import Github
+          import os
+          import time
+
+          g = Github("${{ secrets._GITHUB_TOKEN }}")
+          repo = g.get_repo("${{ github.repository }}")
+
+          # Fetch the default branch name
+          default_branch_name = repo.default_branch
+          default_branch = repo.get_branch(default_branch_name)
+
+          # Initialize counters
+          updated_branches = 0
+          up_to_date_branches = 0
+          errors = 0
+
+          for pr in repo.get_pulls(state='open', sort='created'):
+              try:
+                  # Label PRs as popular for positive reactions
+                  reactions = pr.as_issue().get_reactions()
+                  if sum([(1 if r.content not in {"-1", "confused"} else 0) for r in reactions]) > 5:
+                      pr.set_labels(*("popular",) + tuple(l.name for l in pr.get_labels()))
+
+                  # Get full names for repositories and branches
+                  base_repo_name = repo.full_name
+                  head_repo_name = pr.head.repo.full_name
+                  base_branch_name = pr.base.ref
+                  head_branch_name = pr.head.ref
+
+                  # Check if PR is behind the default branch
+                  comparison = repo.compare(default_branch.commit.sha, pr.head.sha)
+                  if comparison.behind_by > 0:
+                      print(f"⚠️ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is behind {default_branch_name} by {comparison.behind_by} commit(s).")
+
+                      # Attempt to update the branch
+                      try:
+                          success = pr.update_branch()
+                          assert success, "Branch update failed"
+                          print(f"✅ Successfully merged '{default_branch_name}' into PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}).")
+                          updated_branches += 1
+                          time.sleep(10)  # rate limit merges
+                      except Exception as update_error:
+                          print(f"❌ Could not update PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}): {update_error}")
+                          errors += 1
+                  else:
+                      print(f"✅ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is already up to date with {default_branch_name}, no merge required.")
+                      up_to_date_branches += 1
+              except Exception as e:
+                  print(f"❌ Could not process PR #{pr.number}: {e}")
+                  errors += 1
+
+          # Print summary
+          print("\n\nSummary:")
+          print(f"Branches updated: {updated_branches}")
+          print(f"Branches already up-to-date: {up_to_date_branches}")
+          print(f"Total errors: {errors}")

ultralytics-main/.github/workflows/mirror.yml

@@ -0,0 +1,46 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# This action mirrors the Ultralytics repository to other platforms like GitLab.
+# It runs only when the main branch is updated by the repository owner.
+# Additional platforms can be added by uncommenting the relevant sections.
+
+name: Mirror Repository
+
+permissions:
+  contents: read
+
+on:
+  # push:
+  #   branches:
+  #     - main
+  workflow_dispatch:
+
+jobs:
+  mirror:
+    runs-on: ubuntu-latest
+    if: github.repository == 'ultralytics/ultralytics' && github.actor == 'glenn-jocher'
+    steps:
+      - name: Checkout Source Repository (${{ github.repository }})
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0 # Fetch all history for mirroring
+      - name: Run Git Config
+        run: |
+          git config --global user.name "UltralyticsAssistant"
+          git config --global user.email "[email protected]"
+      - name: Push to DagsHub
+        run: |
+          git remote add dagshub https://glenn-jocher:${{ secrets.DAGSHUB_TOKEN }}@dagshub.com/Ultralytics/ultralytics.git
+          git push dagshub main --force
+      # - name: Push to Gitee
+      #   run: |
+      #     git remote add gitee https://ultralytics:${{ secrets.GITEE_TOKEN }}@gitee.com/ultralytics/ultralytics.git
+      #     git push gitee main --force
+      # - name: Push to GitCode
+      #   run: |
+      #     git remote add gitcode https://ultralytics:${{ secrets.GITCODE_TOKEN }}@gitcode.net/ultralytics/ultralytics.git
+      #     git push gitcode main --force
+      # - name: Push to Bitbucket
+      #   run: |
+      #     git remote add bitbucket https://ultralytics:${{ secrets.BITBUCKET_APP_PASSWORD }}@bitbucket.org/ultralytics/ultralytics.git
+      #     git push bitbucket main --force

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

@@ -0,0 +1,153 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Publish pip package to PyPI https://pypi.org/project/ultralytics/
+
+name: Publish to PyPI
+
+on:
+  push:
+    branches: [main]
+  workflow_dispatch:
+    inputs:
+      pypi:
+        type: boolean
+        description: Publish to PyPI
+
+jobs:
+  check:
+    if: github.repository == 'ultralytics/ultralytics' && github.actor == 'glenn-jocher'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    outputs:
+      increment: ${{ steps.check_pypi.outputs.increment }}
+      current_tag: ${{ steps.check_pypi.outputs.current_tag }}
+      previous_tag: ${{ steps.check_pypi.outputs.previous_tag }}
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.x"
+      - uses: astral-sh/setup-uv@v7
+      - run: uv pip install --system --no-cache ultralytics-actions
+      - id: check_pypi
+        shell: python
+        run: |
+          import os
+          from actions.utils import check_pypi_version
+          local_version, online_version, publish = check_pypi_version()
+          os.system(f'echo "increment={publish}" >> $GITHUB_OUTPUT')
+          os.system(f'echo "current_tag=v{local_version}" >> $GITHUB_OUTPUT')
+          os.system(f'echo "previous_tag=v{online_version}" >> $GITHUB_OUTPUT')
+          if publish:
+              print('Ready to publish new version to PyPI ✅.')
+      - name: Tag and Release
+        if: steps.check_pypi.outputs.increment == 'True'
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          CURRENT_TAG: ${{ steps.check_pypi.outputs.current_tag }}
+          PREVIOUS_TAG: ${{ steps.check_pypi.outputs.previous_tag }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        run: |
+          git config --global user.name "UltralyticsAssistant"
+          git config --global user.email "[email protected]"
+          git tag -a "$CURRENT_TAG" -m "$(git log -1 --pretty=%B)"
+          git push origin "$CURRENT_TAG"
+          ultralytics-actions-summarize-release
+          uv cache prune --ci
+
+  build:
+    needs: check
+    if: needs.check.outputs.increment == 'True'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.x"
+      - uses: astral-sh/setup-uv@v7
+      - run: uv pip install --system --no-cache build
+      - run: python -m build
+      - uses: actions/upload-artifact@v5
+        with:
+          name: dist
+          path: dist/
+      - run: uv cache prune --ci
+
+  publish:
+    needs: [check, build]
+    if: needs.check.outputs.increment == 'True'
+    runs-on: ubuntu-latest
+    environment: # for GitHub Deployments tab
+      name: Release - PyPI
+      url: https://pypi.org/p/ultralytics
+    permissions:
+      id-token: write # for PyPI trusted publishing
+    steps:
+      - uses: actions/download-artifact@v6
+        with:
+          name: dist
+          path: dist/
+      - uses: pypa/gh-action-pypi-publish@release/v1
+
+  sbom:
+    needs: [check, build, publish]
+    if: needs.check.outputs.increment == 'True'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.x"
+      - uses: astral-sh/setup-uv@v7
+      - run: |
+          uv venv sbom-env
+          uv pip install -e .
+        env:
+          VIRTUAL_ENV: sbom-env
+      - uses: anchore/sbom-action@v0
+        with:
+          format: spdx-json
+          output-file: sbom.spdx.json
+          path: sbom-env
+      - run: gh release upload ${{ needs.check.outputs.current_tag }} sbom.spdx.json
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  notify:
+    needs: [check, publish]
+    if: always() && needs.check.outputs.increment == 'True'
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    steps:
+      - uses: actions/checkout@v6
+      - name: Extract PR Details
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          PR_JSON=$(gh pr list --search "${GITHUB_SHA}" --state merged --json number,title --jq '.[0]')
+          PR_NUMBER=$(echo "${PR_JSON}" | jq -r '.number')
+          PR_TITLE=$(echo "${PR_JSON}" | jq -r '.title' | sed 's/"/\\"/g')
+          echo "PR_NUMBER=${PR_NUMBER}" >> "${GITHUB_ENV}"
+          echo "PR_TITLE=${PR_TITLE}" >> "${GITHUB_ENV}"
+      - name: Notify Success
+        if: needs.publish.result == 'success' && github.event_name == 'push'
+        uses: slackapi/[email protected]
+        with:
+          webhook-type: incoming-webhook
+          webhook: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
+          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:* ${{ github.actor }}\n*Event:* NEW `${{ github.repository }} ${{ needs.check.outputs.current_tag }}` pip package published 😃\n*Job Status:* ${{ job.status }}\n*Pull Request:* <https://github.com/${{ github.repository }}/pull/${{ env.PR_NUMBER }}> ${{ env.PR_TITLE }}\n"
+      - name: Notify Failure
+        if: needs.publish.result != 'success'
+        uses: slackapi/[email protected]
+        with:
+          webhook-type: incoming-webhook
+          webhook: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
+          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"

ultralytics-main/.github/workflows/stale.yml

@@ -0,0 +1,55 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+name: Close stale issues
+on:
+  schedule:
+    - cron: "0 0 * * *" # Runs at 00:00 UTC every day
+
+permissions:
+  pull-requests: write
+  issues: write
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v10
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+
+          stale-issue-message: |
+            👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
+
+            For additional resources and information, please see the links below:
+
+            - **Docs**: https://docs.ultralytics.com
+            - **HUB**: https://hub.ultralytics.com
+            - **Community**: https://community.ultralytics.com
+
+            Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
+
+            Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
+
+          stale-pr-message: |
+            👋 Hello there! We wanted to let you know that we've decided to close this pull request due to inactivity. We appreciate the effort you put into contributing to our project, but unfortunately, not all contributions are suitable or aligned with our product roadmap.
+
+            We hope you understand our decision, and please don't let it discourage you from contributing to open source projects in the future. We value all of our community members and their contributions, and we encourage you to keep exploring new projects and ways to get involved.
+
+            For additional resources and information, please see the links below:
+
+            - **Docs**: https://docs.ultralytics.com
+            - **HUB**: https://hub.ultralytics.com
+            - **Community**: https://community.ultralytics.com
+
+            Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
+
+          ignore-pr-updates: true
+          remove-pr-stale-when-updated: false
+          exempt-all-assignees: true
+          days-before-issue-stale: 30
+          days-before-issue-close: 10
+          days-before-pr-stale: 90
+          days-before-pr-close: 30
+          exempt-issue-labels: "documentation,tutorial,TODO"
+          exempt-pr-labels: "TODO"
+          operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.

ultralytics-main/.gitignore

@@ -0,0 +1,200 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+requirements.txt
+setup.py
+ultralytics.egg-info
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other info into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+mlruns/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Profiling
+*.pclprof
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+uv.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+.idea
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# VSCode project settings
+.vscode/
+.devcontainer/
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# datasets and projects (ignore /datasets dir at root only to allow for docs/en/datasets dir)
+/datasets
+runs/
+wandb/
+.DS_Store
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+weights/
+*.weights
+*.pt
+*.ts
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.mlpackage
+*.torchscript
+*.tflite
+*.h5
+*.mnn
+*_saved_model/
+*_web_model/
+*_openvino_model/
+*_paddle_model/
+*_ncnn_model/
+*_imx_model/
+pnnx*
+*.rknn
+
+# Autogenerated files for tests
+/ultralytics/assets/
+
+# calibration image
+calibration_*.npy
+
+# Videos and Pictures
+*.mp4
+*.avi
+*.mov
+*.mkv
+*.webm
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.tiff
+*.gif
+*.svg
+*.webp
+*.heic
+*.ico
+*.raw

ultralytics-main/CITATION.cff

@@ -0,0 +1,26 @@
+# This CITATION.cff file was generated with https://bit.ly/cffinit
+
+cff-version: 1.2.0
+title: Ultralytics YOLO
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Glenn
+    family-names: Jocher
+    affiliation: Ultralytics
+    orcid: "https://orcid.org/0000-0001-5950-6979"
+  - family-names: Qiu
+    given-names: Jing
+    affiliation: Ultralytics
+    orcid: "https://orcid.org/0000-0003-3783-7069"
+  - given-names: Ayush
+    family-names: Chaurasia
+    affiliation: Ultralytics
+    orcid: "https://orcid.org/0000-0002-7603-6750"
+repository-code: "https://github.com/ultralytics/ultralytics"
+url: "https://ultralytics.com"
+license: AGPL-3.0
+version: 8.0.0
+date-released: "2023-01-10"

ultralytics-main/CONTRIBUTING.md

@@ -0,0 +1,241 @@
+<a href="https://www.ultralytics.com/" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
+
+# Contributing to Ultralytics Open-Source Projects
+
+Welcome! We're thrilled that you're considering contributing to our [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) projects. Your involvement not only helps enhance the quality of our repositories but also benefits the entire [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. This guide provides clear guidelines and best practices to help you get started.
+
+[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)
+
+## 🤝 Code of Conduct
+
+To ensure a welcoming and inclusive environment for everyone, all contributors must adhere to our [Code of Conduct](https://docs.ultralytics.com/help/code-of-conduct/). **Respect**, **kindness**, and **professionalism** are at the heart of our community.
+
+## 🚀 Contributing via Pull Requests
+
+We greatly appreciate contributions in the form of [pull requests (PRs)](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests). To make the review process as smooth as possible, please follow these steps:
+
+1. **[Fork the repository](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo):** Start by forking the relevant Ultralytics repository (e.g., [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)) to your GitHub account.
+2. **[Create a branch](https://docs.github.com/en/desktop/making-changes-in-a-branch/managing-branches-in-github-desktop):** Create a new branch in your forked repository with a clear, descriptive name reflecting your changes (e.g., `fix-issue-123`, `add-feature-xyz`).
+3. **Make your changes:** Implement your improvements or fixes. Ensure your code adheres to the project's style guidelines and doesn't introduce new errors or warnings.
+4. **Test your changes:** Before submitting, test your changes locally to confirm they work as expected and don't cause [regressions](https://en.wikipedia.org/wiki/Software_regression). Add tests if you're introducing new functionality.
+5. **[Commit your changes](https://docs.github.com/en/desktop/making-changes-in-a-branch/committing-and-reviewing-changes-to-your-project-in-github-desktop):** Commit your changes with concise and descriptive commit messages. If your changes address a specific issue, include the issue number (e.g., `Fix #123: Corrected calculation error.`).
+6. **[Create a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request):** Submit a pull request from your branch to the `main` branch of the original Ultralytics repository. Provide a clear title and a detailed description explaining the purpose and scope of your changes.
+
+### 📝 CLA Signing
+
+Before we can merge your pull request, you must sign our [Contributor License Agreement (CLA)](https://docs.ultralytics.com/help/CLA/). This legal agreement ensures that your contributions are properly licensed, allowing the project to continue being distributed under the [AGPL-3.0 license](https://www.ultralytics.com/legal/agpl-3-0-software-license).
+
+After submitting your pull request, the CLA bot will guide you through the signing process. To sign the CLA, simply add a comment in your PR stating:
+
+```text
+I have read the CLA Document and I sign the CLA
+```
+
+### ✍️ Google-Style Docstrings
+
+When adding new functions or classes, please include [Google-style docstrings](https://google.github.io/styleguide/pyguide.html). These docstrings provide clear, standardized documentation that helps other developers understand and maintain your code.
+
+#### Example Google-style
+
+This example illustrates a Google-style docstring. Ensure that both input and output `types` are always enclosed in parentheses, e.g., `(bool)`.
+
+```python
+def example_function(arg1, arg2=4):
+    """Example function demonstrating Google-style docstrings.
+
+    Args:
+        arg1 (int): The first argument.
+        arg2 (int): The second argument, with a default value of 4.
+
+    Returns:
+        (bool): True if successful, False otherwise.
+
+    Examples:
+        >>> result = example_function(1, 2)  # returns False
+    """
+    if arg1 == arg2:
+        return True
+    return False
+```
+
+#### Example Google-style with type hints
+
+This example includes both a Google-style docstring and [type hints](https://docs.python.org/3/library/typing.html) for arguments and returns, though using either independently is also acceptable.
+
+```python
+def example_function(arg1: int, arg2: int = 4) -> bool:
+    """Example function demonstrating Google-style docstrings.
+
+    Args:
+        arg1: The first argument.
+        arg2: The second argument, with a default value of 4.
+
+    Returns:
+        True if successful, False otherwise.
+
+    Examples:
+        >>> result = example_function(1, 2)  # returns False
+    """
+    if arg1 == arg2:
+        return True
+    return False
+```
+
+#### Example Single-line
+
+For smaller or simpler functions, a single-line docstring may be sufficient. The docstring must use three double-quotes, be a complete sentence, start with a capital letter, and end with a period.
+
+```python
+def example_small_function(arg1: int, arg2: int = 4) -> bool:
+    """Example function with a single-line docstring."""
+    return arg1 == arg2
+```
+
+### ✅ GitHub Actions CI Tests
+
+All pull requests must pass the [GitHub Actions](https://github.com/features/actions) [Continuous Integration](https://docs.ultralytics.com/help/CI/) (CI) tests before they can be merged. These tests include linting, unit tests, and other checks to ensure that your changes meet the project's quality standards. Review the CI output and address any issues that arise.
+
+## ✨ Best Practices for Code Contributions
+
+When contributing code to Ultralytics projects, keep these best practices in mind:
+
+- **Avoid code duplication:** Reuse existing code wherever possible and minimize unnecessary arguments.
+- **Make smaller, focused changes:** Focus on targeted modifications rather than large-scale changes.
+- **Simplify when possible:** Look for opportunities to simplify the code or remove unnecessary parts.
+- **Consider compatibility:** Before making changes, consider whether they might break existing code using Ultralytics.
+- **Use consistent formatting:** Tools like [Ruff Formatter](https://github.com/astral-sh/ruff) can help maintain stylistic consistency.
+- **Add appropriate tests:** Include [tests](https://docs.ultralytics.com/guides/model-testing/) for new features to ensure they work as expected.
+
+## 👀 Reviewing Pull Requests
+
+Reviewing pull requests is another valuable way to contribute. When reviewing PRs:
+
+- **Check for unit tests:** Verify that the PR includes tests for new features or changes.
+- **Review documentation updates:** Ensure [documentation](https://docs.ultralytics.com/) is updated to reflect changes.
+- **Evaluate performance impact:** Consider how changes might affect [performance](https://docs.ultralytics.com/guides/yolo-performance-metrics/).
+- **Verify CI tests:** Confirm all [Continuous Integration tests](https://docs.ultralytics.com/help/CI/) are passing.
+- **Provide constructive feedback:** Offer specific, clear feedback about any issues or concerns.
+- **Recognize effort:** Acknowledge the author's work to maintain a positive collaborative atmosphere.
+
+## 🐞 Reporting Bugs
+
+We highly value bug reports as they help us improve the quality and reliability of our projects. When reporting a bug via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues):
+
+- **Check existing issues:** Search first to see if the bug has already been reported.
+- **Provide a [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum-reproducible-example/):** Create a small, self-contained code snippet that consistently reproduces the issue. This is crucial for efficient debugging.
+- **Describe the environment:** Specify your operating system, Python version, relevant library versions (e.g., [`torch`](https://pytorch.org/), [`ultralytics`](https://github.com/ultralytics/ultralytics)), and hardware ([CPU](https://en.wikipedia.org/wiki/Central_processing_unit)/[GPU](https://www.ultralytics.com/glossary/gpu-graphics-processing-unit)).
+- **Explain expected vs. actual behavior:** Clearly state what you expected to happen and what actually occurred. Include any error messages or tracebacks.
+
+## 📜 License
+
+Ultralytics uses the [GNU Affero General Public License v3.0 (AGPL-3.0)](https://www.ultralytics.com/legal/agpl-3-0-software-license) for its repositories. This license promotes [openness](https://en.wikipedia.org/wiki/Openness), [transparency](https://www.ultralytics.com/glossary/transparency-in-ai), and [collaborative improvement](https://en.wikipedia.org/wiki/Collaborative_software) in software development. It ensures that all users have the freedom to use, modify, and share the software, fostering a strong community of collaboration and innovation.
+
+We encourage all contributors to familiarize themselves with the terms of the [AGPL-3.0 license](https://opensource.org/license/agpl-v3) to contribute effectively and ethically to the Ultralytics open-source community.
+
+## 🌍 Open-Sourcing Your YOLO Project Under AGPL-3.0
+
+Using Ultralytics YOLO models or code in your project? The [AGPL-3.0 license](https://opensource.org/license/agpl-v3) requires that your entire derivative work also be open-sourced under AGPL-3.0. This ensures modifications and larger projects built upon open-source foundations remain open.
+
+### Why AGPL-3.0 Compliance Matters
+
+- **Keeps Software Open:** Ensures that improvements and derivative works benefit the community.
+- **Legal Requirement:** Using AGPL-3.0 licensed code binds your project to its terms.
+- **Fosters Collaboration:** Encourages sharing and transparency.
+
+If you prefer not to open-source your project, consider obtaining an [Enterprise License](https://www.ultralytics.com/license).
+
+### How to Comply with AGPL-3.0
+
+Complying means making the **complete corresponding source code** of your project publicly available under the AGPL-3.0 license.
+
+1. **Choose Your Starting Point:**
+   - **Fork Ultralytics YOLO:** Directly fork the [Ultralytics YOLO repository](https://github.com/ultralytics/ultralytics) if building closely upon it.
+   - **Use Ultralytics Template:** Start with the [Ultralytics template repository](https://github.com/ultralytics/template) for a clean, modular setup integrating YOLO.
+
+2. **License Your Project:**
+   - Add a `LICENSE` file containing the full text of the [AGPL-3.0 license](https://opensource.org/license/agpl-v3).
+   - Add a notice at the top of each source file indicating the license.
+
+3. **Publish Your Source Code:**
+   - Make your **entire project's source code** publicly accessible (e.g., on GitHub). This includes:
+     - The complete larger application or system that incorporates the YOLO model or code.
+     - Any modifications made to the original Ultralytics YOLO code.
+     - Scripts for training, validation, inference.
+     - [Model weights](https://www.ultralytics.com/glossary/model-weights) if modified or fine-tuned.
+     - [Configuration files](https://docs.ultralytics.com/usage/cfg/), environment setups (`requirements.txt`, [`Dockerfiles`](https://docs.docker.com/reference/dockerfile/)).
+     - Backend and frontend code if it's part of a [web application](https://en.wikipedia.org/wiki/Web_application).
+     - Any [third-party libraries](<https://en.wikipedia.org/wiki/Library_(computing)#Third-party>) you've modified.
+     - [Training data](https://www.ultralytics.com/glossary/training-data) if required to run/retrain _and_ redistributable.
+
+4. **Document Clearly:**
+   - Update your `README.md` to state that the project is licensed under AGPL-3.0.
+   - Include clear instructions on how to set up, build, and run your project from the source code.
+   - Attribute Ultralytics YOLO appropriately, linking back to the [original repository](https://github.com/ultralytics/ultralytics). Example:
+     ```markdown
+     This project utilizes code from [Ultralytics YOLO](https://github.com/ultralytics/ultralytics), licensed under AGPL-3.0.
+     ```
+
+### Example Repository Structure
+
+Refer to the [Ultralytics Template Repository](https://github.com/ultralytics/template) for a practical example structure:
+
+```
+my-yolo-project/
+│
+├── LICENSE               # Full AGPL-3.0 license text
+├── README.md             # Project description, setup, usage, license info & attribution
+├── pyproject.toml        # Dependencies (or requirements.txt)
+├── scripts/              # Training/inference scripts
+│   └── train.py
+├── src/                  # Your project's source code
+│   ├── __init__.py
+│   ├── data_loader.py
+│   └── model_wrapper.py  # Code interacting with YOLO
+├── tests/                # Unit/integration tests
+├── configs/              # YAML/JSON config files
+├── docker/               # Dockerfiles, if used
+│   └── Dockerfile
+└── .github/              # GitHub specific files (e.g., workflows for CI)
+    └── workflows/
+        └── ci.yml
+```
+
+By following these guidelines, you ensure compliance with AGPL-3.0, supporting the open-source ecosystem that enables powerful tools like Ultralytics YOLO.
+
+## 🎉 Conclusion
+
+Thank you for your interest in contributing to [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) YOLO projects. Your participation is essential in shaping the future of our software and building a vibrant community of innovation and collaboration. Whether you're enhancing code, reporting bugs, or suggesting new features, your contributions are invaluable.
+
+We're excited to see your ideas come to life and appreciate your commitment to advancing [object detection](https://www.ultralytics.com/glossary/object-detection) technology. Together, let's continue to grow and innovate in this exciting open-source journey. Happy coding! 🚀🌟
+
+## FAQ
+
+### Why should I contribute to Ultralytics YOLO open-source repositories?
+
+Contributing to Ultralytics YOLO open-source repositories improves the software, making it more robust and feature-rich for the entire community. Contributions can include code enhancements, bug fixes, documentation improvements, and new feature implementations. Additionally, contributing allows you to collaborate with other skilled developers and experts in the field, enhancing your own skills and reputation. For details on how to get started, refer to the [Contributing via Pull Requests](#-contributing-via-pull-requests) section.
+
+### How do I sign the Contributor License Agreement (CLA) for Ultralytics YOLO?
+
+To sign the Contributor License Agreement (CLA), follow the instructions provided by the CLA bot after submitting your pull request. This process ensures that your contributions are properly licensed under the AGPL-3.0 license, maintaining the legal integrity of the open-source project. Add a comment in your pull request stating:
+
+```text
+I have read the CLA Document and I sign the CLA
+```
+
+For more information, see the [CLA Signing](#-cla-signing) section.
+
+### What are Google-style docstrings, and why are they required for Ultralytics YOLO contributions?
+
+Google-style docstrings provide clear, concise documentation for functions and classes, improving code readability and maintainability. These docstrings outline the function's purpose, arguments, and return values with specific formatting rules. When contributing to Ultralytics YOLO, following Google-style docstrings ensures that your additions are well-documented and easily understood. For examples and guidelines, visit the [Google-Style Docstrings](#-google-style-docstrings) section.
+
+### How can I ensure my changes pass the GitHub Actions CI tests?
+
+Before your pull request can be merged, it must pass all GitHub Actions Continuous Integration (CI) tests. These tests include linting, unit tests, and other checks to ensure the code meets the project's quality standards. Review the CI output and fix any issues. For detailed information on the CI process and troubleshooting tips, see the [GitHub Actions CI Tests](#-github-actions-ci-tests) section.
+
+### How do I report a bug in Ultralytics YOLO repositories?
+
+To report a bug, provide a clear and concise [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum-reproducible-example/) along with your bug report. This helps developers quickly identify and fix the issue. Ensure your example is minimal yet sufficient to replicate the problem. For more detailed steps on reporting bugs, refer to the [Reporting Bugs](#-reporting-bugs) section.
+
+### What does the AGPL-3.0 license mean if I use Ultralytics YOLO in my own project?
+
+If you use Ultralytics YOLO code or models (licensed under AGPL-3.0) in your project, the AGPL-3.0 license requires that your entire project (the derivative work) must also be licensed under AGPL-3.0 and its complete source code must be made publicly available. This ensures that the open-source nature of the software is preserved throughout its derivatives. If you cannot meet these requirements, you need to obtain an [Enterprise License](https://www.ultralytics.com/license). See the [Open-Sourcing Your Project](#-open-sourcing-your-yolo-project-under-agpl-30) section for details.

ultralytics-main/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
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+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
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+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
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+
+Also add information on how to contact you by electronic and paper mail.
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+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

ultralytics-main/README.md

@@ -0,0 +1,285 @@
+<div align="center">
+  <p>
+    <a href="https://www.ultralytics.com/events/yolovision?utm_source=github&utm_medium=org&utm_campaign=yv25_event" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
+  </p>
+
+[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/) <br>
+
+<div>
+    <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://clickpy.clickhouse.com/dashboard/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
+    <a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
+    <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
+    <a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
+    <br>
+    <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics on Gradient"></a>
+    <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Ultralytics In Colab"></a>
+    <a href="https://www.kaggle.com/models/ultralytics/yolo11"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
+    <a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
+</div>
+</div>
+<br>
+
+[Ultralytics](https://www.ultralytics.com/) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https://www.ultralytics.com/yolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. They excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/) tasks.
+
+Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!
+
+Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+<a href="https://docs.ultralytics.com/models/yolo11/" target="_blank">
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="YOLO11 performance plots">
+</a>
+
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+
+## 📄 Documentation
+
+See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full [Ultralytics Docs](https://docs.ultralytics.com/).
+
+<details open>
+<summary>Install</summary>
+
+Install the `ultralytics` package, including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml), in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Ultralytics Downloads](https://static.pepy.tech/badge/ultralytics)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/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 building from source via Git, please consult the [Quickstart Guide](https://docs.ultralytics.com/quickstart/).
+
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics) [![Ultralytics Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
+
+</details>
+
+<details open>
+<summary>Usage</summary>
+
+### CLI
+
+You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command:
+
+```bash
+# Predict using a pretrained YOLO model (e.g., YOLO11n) on an image
+yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for more examples.
+
+### Python
+
+Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same [configuration arguments](https://docs.ultralytics.com/usage/cfg/) as the CLI:
+
+```python
+from ultralytics import YOLO
+
+# Load a pretrained YOLO11n model
+model = YOLO("yolo11n.pt")
+
+# Train the model on the COCO8 dataset for 100 epochs
+train_results = model.train(
+    data="coco8.yaml",  # Path to dataset configuration file
+    epochs=100,  # Number of training epochs
+    imgsz=640,  # Image size for training
+    device="cpu",  # Device to run on (e.g., 'cpu', 0, [0,1,2,3])
+)
+
+# Evaluate the model's performance on the validation set
+metrics = model.val()
+
+# Perform object detection on an image
+results = model("path/to/image.jpg")  # Predict on an image
+results[0].show()  # Display results
+
+# Export the model to ONNX format for deployment
+path = model.export(format="onnx")  # Returns the path to the exported model
+```
+
+Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python/).
+
+</details>
+
+## ✨ Models
+
+Ultralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https://docs.ultralytics.com/models/yolov3/) to the latest [YOLO11](https://docs.ultralytics.com/models/yolo11/). The tables below showcase YOLO11 models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset for [Detection](https://docs.ultralytics.com/tasks/detect/), [Segmentation](https://docs.ultralytics.com/tasks/segment/), and [Pose Estimation](https://docs.ultralytics.com/tasks/pose/). Additionally, [Classification](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset are available. [Tracking](https://docs.ultralytics.com/modes/track/) mode is compatible with all Detection, Segmentation, and Pose models. All [Models](https://docs.ultralytics.com/models/) are automatically downloaded from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) upon first use.
+
+<a href="https://docs.ultralytics.com/tasks/" target="_blank">
+    <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
+</a>
+<br>
+<br>
+
+<details open><summary>Detection (COCO)</summary>
+
+Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples. These models are trained on the [COCO dataset](https://cocodataset.org/), featuring 80 object classes.
+
+| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640                   | 39.5                 | 56.1 ± 0.8                     | 1.5 ± 0.0                           | 2.6                | 6.5               |
+| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640                   | 47.0                 | 90.0 ± 1.2                     | 2.5 ± 0.0                           | 9.4                | 21.5              |
+| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640                   | 51.5                 | 183.2 ± 2.0                    | 4.7 ± 0.1                           | 20.1               | 68.0              |
+| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640                   | 53.4                 | 238.6 ± 1.4                    | 6.2 ± 0.1                           | 25.3               | 86.9              |
+| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640                   | 54.7                 | 462.8 ± 6.7                    | 11.3 ± 0.2                          | 56.9               | 194.9             |
+
+- **mAP<sup>val</sup>** values refer to single-model single-scale performance on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val detect data=coco.yaml device=0`
+- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Segmentation (COCO)</summary>
+
+Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples. These models are trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), including 80 classes.
+
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640                   | 38.9                 | 32.0                  | 65.9 ± 1.1                     | 1.8 ± 0.0                           | 2.9                | 9.7               |
+| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640                   | 46.6                 | 37.8                  | 117.6 ± 4.9                    | 2.9 ± 0.0                           | 10.1               | 33.0              |
+| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640                   | 51.5                 | 41.5                  | 281.6 ± 1.2                    | 6.3 ± 0.1                           | 22.4               | 113.2             |
+| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                   | 53.4                 | 42.9                  | 344.2 ± 3.2                    | 7.8 ± 0.2                           | 27.6               | 132.2             |
+| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                   | 54.7                 | 43.8                  | 664.5 ± 3.2                    | 15.8 ± 0.7                          | 62.1               | 296.4             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val segment data=coco.yaml device=0`
+- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Classification (ImageNet)</summary>
+
+Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples. These models are trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), covering 1000 classes.
+
+| Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 224 |
+| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
+| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224                   | 70.0             | 89.4             | 5.0 ± 0.3                      | 1.1 ± 0.0                           | 2.8                | 0.5                      |
+| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224                   | 75.4             | 92.7             | 7.9 ± 0.2                      | 1.3 ± 0.0                           | 6.7                | 1.6                      |
+| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224                   | 77.3             | 93.9             | 17.2 ± 0.4                     | 2.0 ± 0.0                           | 11.6               | 4.9                      |
+| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224                   | 78.3             | 94.3             | 23.2 ± 0.3                     | 2.8 ± 0.0                           | 14.1               | 6.2                      |
+| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224                   | 79.5             | 94.9             | 41.4 ± 0.9                     | 3.8 ± 0.0                           | 29.6               | 13.6                     |
+
+- **acc** values represent model accuracy on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce with `yolo val classify data=path/to/ImageNet device=0`
+- **Speed** metrics are averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Pose (COCO)</summary>
+
+See the [Pose Estimation Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples. These models are trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), focusing on the 'person' class.
+
+| Model                                                                                          | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640                   | 50.0                  | 81.0               | 52.4 ± 0.5                     | 1.7 ± 0.0                           | 2.9                | 7.4               |
+| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640                   | 58.9                  | 86.3               | 90.5 ± 0.6                     | 2.6 ± 0.0                           | 9.9                | 23.1              |
+| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640                   | 64.9                  | 89.4               | 187.3 ± 0.8                    | 4.9 ± 0.1                           | 20.9               | 71.4              |
+| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640                   | 66.1                  | 89.9               | 247.7 ± 1.1                    | 6.4 ± 0.1                           | 26.1               | 90.3              |
+| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640                   | 69.5                  | 91.1               | 488.0 ± 13.9                   | 12.1 ± 0.2                          | 58.8               | 202.8             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on the [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details. <br>Reproduce with `yolo val pose data=coco-pose.yaml device=0`
+- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Oriented Bounding Boxes (DOTAv1)</summary>
+
+Check the [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples. These models are trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), including 15 classes.
+
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024                  | 78.4               | 117.6 ± 0.8                    | 4.4 ± 0.0                           | 2.7                | 16.8              |
+| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024                  | 79.5               | 219.4 ± 4.0                    | 5.1 ± 0.0                           | 9.7                | 57.1              |
+| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024                  | 80.9               | 562.8 ± 2.9                    | 10.1 ± 0.4                          | 20.9               | 182.8             |
+| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024                  | 81.0               | 712.5 ± 5.0                    | 13.5 ± 0.6                          | 26.1               | 231.2             |
+| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024                  | 81.3               | 1408.6 ± 7.7                   | 28.6 ± 1.0                          | 58.8               | 519.1             |
+
+- **mAP<sup>test</sup>** values are for single-model multiscale performance on the [DOTAv1 test set](https://captain-whu.github.io/DOTA/dataset.html). <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to the [DOTA evaluation server](https://captain-whu.github.io/DOTA/evaluation.html).
+- **Speed** metrics are averaged over [DOTAv1 val images](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10) using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
+
+</details>
+
+## 🧩 Integrations
+
+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 partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/).
+
+<a href="https://docs.ultralytics.com/integrations/" target="_blank">
+    <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
+</a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://www.ultralytics.com/hub">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.ultralytics.com/integrations/weights-biases/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="Weights & Biases logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.ultralytics.com/integrations/comet/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.ultralytics.com/integrations/neural-magic/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
+</div>
+
+|                                                       Ultralytics HUB 🌟                                                        |                                                          Weights & Biases                                                           |                                                                              Comet                                                                              |                                                        Neural Magic                                                         |
+| :-----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
+| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://hub.ultralytics.com/). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |
+
+## 🌟 Ultralytics HUB
+
+Experience seamless AI with [Ultralytics HUB](https://hub.ultralytics.com/), the all-in-one platform for data visualization, training YOLO models, and deployment—no coding required. Transform images into actionable insights and bring your AI visions to life effortlessly using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** today!
+
+<a href="https://www.ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
+
+## 🤝 Contribute
+
+We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. We also welcome your feedback—share your experience by completing our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge **Thank You** 🙏 to everyone who contributes!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=1280 -->
+
+[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)
+
+We look forward to your contributions to help make the Ultralytics ecosystem even better!
+
+## 📜 License
+
+Ultralytics offers two licensing options to suit different needs:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license/agpl-v3) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for full details.
+- **Ultralytics Enterprise License**: Designed for commercial use, this license allows for the seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. If your use case involves commercial deployment, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+## 📞 Contact
+
+For bug reports and feature requests related to Ultralytics software, please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For questions, discussions, and community support, join our active communities on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/). We're here to help with all things Ultralytics!
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>

ultralytics-main/README.zh-CN.md

@@ -0,0 +1,285 @@
+<div align="center">
+  <p>
+    <a href="https://www.ultralytics.com/events/yolovision?utm_source=github&utm_medium=org&utm_campaign=yv25_event" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
+  </p>
+
+[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/) <br>
+
+<div>
+    <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://clickpy.clickhouse.com/dashboard/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
+    <a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
+    <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
+    <a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
+    <br>
+    <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics on Gradient"></a>
+    <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Ultralytics In Colab"></a>
+    <a href="https://www.kaggle.com/models/ultralytics/yolo11"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
+    <a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
+</div>
+</div>
+<br>
+
+[Ultralytics](https://www.ultralytics.com/) 基于多年在计算机视觉和人工智能领域的基础研究，创造了尖端的、最先进的 (SOTA) [YOLO 模型](https://www.ultralytics.com/yolo)。我们的模型不断更新以提高性能和灵活性，具有**速度快**、**精度高**和**易于使用**的特点。它们在[目标检测](https://docs.ultralytics.com/tasks/detect/)、[跟踪](https://docs.ultralytics.com/modes/track/)、[实例分割](https://docs.ultralytics.com/tasks/segment/)、[图像分类](https://docs.ultralytics.com/tasks/classify/)和[姿态估计](https://docs.ultralytics.com/tasks/pose/)任务中表现出色。
+
+在 [Ultralytics 文档](https://docs.ultralytics.com/)中查找详细文档。通过 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose) 获取支持。加入 [Discord](https://discord.com/invite/ultralytics)、[Reddit](https://www.reddit.com/r/ultralytics/) 和 [Ultralytics 社区论坛](https://community.ultralytics.com/)参与讨论！
+
+如需商业用途，请在 [Ultralytics 授权许可](https://www.ultralytics.com/license)申请企业许可证。
+
+<a href="https://docs.ultralytics.com/models/yolo11/" target="_blank">
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="YOLO11 performance plots">
+</a>
+
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+
+## 📄 文档
+
+请参阅下文了解快速安装和使用示例。有关训练、验证、预测和部署的全面指南，请参阅我们的完整 [Ultralytics 文档](https://docs.ultralytics.com/)。
+
+<details open>
+<summary>安装</summary>
+
+在 [**Python>=3.8**](https://www.python.org/) 环境中安装 `ultralytics` 包，包括所有[依赖项](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml)，并确保 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。
+
+[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Ultralytics Downloads](https://static.pepy.tech/badge/ultralytics)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)
+
+```bash
+pip install ultralytics
+```
+
+有关其他安装方法，包括 [Conda](https://anaconda.org/conda-forge/ultralytics)、[Docker](https://hub.docker.com/r/ultralytics/ultralytics) 以及通过 Git 从源代码构建，请查阅[快速入门指南](https://docs.ultralytics.com/quickstart/)。
+
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics) [![Ultralytics Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
+
+</details>
+
+<details open>
+<summary>使用方法</summary>
+
+### CLI
+
+您可以直接通过命令行界面 (CLI) 使用 `yolo` 命令来运行 Ultralytics YOLO：
+
+```bash
+# 使用预训练的 YOLO 模型 (例如 YOLO11n) 对图像进行预测
+yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+`yolo` 命令支持各种任务和模式，并接受额外的参数，如 `imgsz=640`。浏览 YOLO [CLI 文档](https://docs.ultralytics.com/usage/cli/)获取更多示例。
+
+### Python
+
+Ultralytics YOLO 也可以直接集成到您的 Python 项目中。它接受与 CLI 相同的[配置参数](https://docs.ultralytics.com/usage/cfg/)：
+
+```python
+from ultralytics import YOLO
+
+# 加载一个预训练的 YOLO11n 模型
+model = YOLO("yolo11n.pt")
+
+# 在 COCO8 数据集上训练模型 100 个周期
+train_results = model.train(
+    data="coco8.yaml",  # 数据集配置文件路径
+    epochs=100,  # 训练周期数
+    imgsz=640,  # 训练图像尺寸
+    device="cpu",  # 运行设备 (例如 'cpu', 0, [0,1,2,3])
+)
+
+# 评估模型在验证集上的性能
+metrics = model.val()
+
+# 对图像执行目标检测
+results = model("path/to/image.jpg")  # 对图像进行预测
+results[0].show()  # 显示结果
+
+# 将模型导出为 ONNX 格式以进行部署
+path = model.export(format="onnx")  # 返回导出模型的路径
+```
+
+在 YOLO [Python 文档](https://docs.ultralytics.com/usage/python/)中发现更多示例。
+
+</details>
+
+## ✨ 模型
+
+Ultralytics 支持广泛的 YOLO 模型，从早期的版本如 [YOLOv3](https://docs.ultralytics.com/models/yolov3/) 到最新的 [YOLO11](https://docs.ultralytics.com/models/yolo11/)。下表展示了在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上预训练的 YOLO11 模型，用于[检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/)和[姿态估计](https://docs.ultralytics.com/tasks/pose/)任务。此外，还提供了在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的[分类](https://docs.ultralytics.com/tasks/classify/)模型。[跟踪](https://docs.ultralytics.com/modes/track/)模式与所有检测、分割和姿态模型兼容。所有[模型](https://docs.ultralytics.com/models/)在首次使用时都���自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
+
+<a href="https://docs.ultralytics.com/tasks/" target="_blank">
+    <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
+</a>
+<br>
+<br>
+
+<details open><summary>检测 (COCO)</summary>
+
+浏览[检测文档](https://docs.ultralytics.com/tasks/detect/)获取使用示例。这些模型在 [COCO 数据集](https://cocodataset.org/)上训练，包含 80 个对象类别。
+
+| 模型                                                                                 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
+| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
+| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640                 | 39.5                 | 56.1 ± 0.8                      | 1.5 ± 0.0                            | 2.6                 | 6.5                  |
+| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640                 | 47.0                 | 90.0 ± 1.2                      | 2.5 ± 0.0                            | 9.4                 | 21.5                 |
+| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640                 | 51.5                 | 183.2 ± 2.0                     | 4.7 ± 0.1                            | 20.1                | 68.0                 |
+| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640                 | 53.4                 | 238.6 ± 1.4                     | 6.2 ± 0.1                            | 25.3                | 86.9                 |
+| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640                 | 54.7                 | 462.8 ± 6.7                     | 11.3 ± 0.2                           | 56.9                | 194.9                |
+
+- **mAP<sup>val</sup>** 值指的是在 [COCO val2017](https://cocodataset.org/) 数据集上的单模型单尺度性能。详见 [YOLO 性能指标](https://docs.ultralytics.com/guides/yolo-performance-metrics/)。<br>使用 `yolo val detect data=coco.yaml device=0` 复现结果。
+- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 COCO val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现结果。
+
+</details>
+
+<details><summary>分割 (COCO)</summary>
+
+请参阅[分割文档](https://docs.ultralytics.com/tasks/segment/)获取使用示例。这些模型在 [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/) 数据集上训练，包含 80 个类别。
+
+| 模型                                                                                         | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
+| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
+| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640                 | 38.9                 | 32.0                  | 65.9 ± 1.1                      | 1.8 ± 0.0                            | 2.9                 | 9.7                  |
+| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640                 | 46.6                 | 37.8                  | 117.6 ± 4.9                     | 2.9 ± 0.0                            | 10.1                | 33.0                 |
+| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640                 | 51.5                 | 41.5                  | 281.6 ± 1.2                     | 6.3 ± 0.1                            | 22.4                | 113.2                |
+| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                 | 53.4                 | 42.9                  | 344.2 ± 3.2                     | 7.8 ± 0.2                            | 27.6                | 132.2                |
+| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                 | 54.7                 | 43.8                  | 664.5 ± 3.2                     | 15.8 ± 0.7                           | 62.1                | 296.4                |
+
+- **mAP<sup>val</sup>** 值指的是在 [COCO val2017](https://cocodataset.org/) 数据集上的单模型单尺度性能。详见 [YOLO 性能指标](https://docs.ultralytics.com/guides/yolo-performance-metrics/)。<br>使用 `yolo val segment data=coco.yaml device=0` 复现结果。
+- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 COCO val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val segment data=coco.yaml batch=1 device=0|cpu` 复现结果。
+
+</details>
+
+<details><summary>分类 (ImageNet)</summary>
+
+请查阅[分类文档](https://docs.ultralytics.com/tasks/classify/)获取使用示例。这些模型在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上训练，涵盖 1000 个类别。
+
+| 模型                                                                                         | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) @ 224 |
+| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ------------------------------- | ------------------------------------ | ------------------- | -------------------------- |
+| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224                 | 70.0             | 89.4             | 5.0 ± 0.3                       | 1.1 ± 0.0                            | 2.8                 | 0.5                        |
+| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224                 | 75.4             | 92.7             | 7.9 ± 0.2                       | 1.3 ± 0.0                            | 6.7                 | 1.6                        |
+| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224                 | 77.3             | 93.9             | 17.2 ± 0.4                      | 2.0 ± 0.0                            | 11.6                | 4.9                        |
+| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224                 | 78.3             | 94.3             | 23.2 ± 0.3                      | 2.8 ± 0.0                            | 14.1                | 6.2                        |
+| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224                 | 79.5             | 94.9             | 41.4 ± 0.9                      | 3.8 ± 0.0                            | 29.6                | 13.6                       |
+
+- **acc** 值表示模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。<br>使用 `yolo val classify data=path/to/ImageNet device=0` 复现结果。
+- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 ImageNet val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现结果。
+
+</details>
+
+<details><summary>姿态估计 (COCO)</summary>
+
+请参阅[姿态估计文档](https://docs.ultralytics.com/tasks/pose/)获取使用示例。这些模型在 [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/) 数据集上训练，专注于 'person' 类别。
+
+| 模型                                                                                           | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
+| ---------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
+| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640                 | 50.0                  | 81.0               | 52.4 ± 0.5                      | 1.7 ± 0.0                            | 2.9                 | 7.4                  |
+| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640                 | 58.9                  | 86.3               | 90.5 ± 0.6                      | 2.6 ± 0.0                            | 9.9                 | 23.1                 |
+| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640                 | 64.9                  | 89.4               | 187.3 ± 0.8                     | 4.9 ± 0.1                            | 20.9                | 71.4                 |
+| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640                 | 66.1                  | 89.9               | 247.7 ± 1.1                     | 6.4 ± 0.1                            | 26.1                | 90.3                 |
+| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640                 | 69.5                  | 91.1               | 488.0 ± 13.9                    | 12.1 ± 0.2                           | 58.8                | 202.8                |
+
+- **mAP<sup>val</sup>** 值指的是在 [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco/) 数据集上的单模型单尺度性能。详见 [YOLO 性能指标](https://docs.ultralytics.com/guides/yolo-performance-metrics/)。<br>使用 `yolo val pose data=coco-pose.yaml device=0` 复现结果。
+- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 COCO val 图像进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>使用 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现结果。
+
+</details>
+
+<details><summary>定向边界框 (DOTAv1)</summary>
+
+请查阅 [OBB 文档](https://docs.ultralytics.com/tasks/obb/)获取使用示例。这些模型在 [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10) 数据集上训练，包含 15 个类别。
+
+| 模型                                                                                         | 尺寸<br><sup>(像素) | mAP<sup>test<br>50 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>T4 TensorRT10<br>(毫秒) | 参数<br><sup>(百万) | FLOPs<br><sup>(十亿) |
+| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ------------------------------- | ------------------------------------ | ------------------- | -------------------- |
+| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024                | 78.4               | 117.6 ± 0.8                     | 4.4 ± 0.0                            | 2.7                 | 16.8                 |
+| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024                | 79.5               | 219.4 ± 4.0                     | 5.1 ± 0.0                            | 9.7                 | 57.1                 |
+| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024                | 80.9               | 562.8 ± 2.9                     | 10.1 ± 0.4                           | 20.9                | 182.8                |
+| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024                | 81.0               | 712.5 ± 5.0                     | 13.5 ± 0.6                           | 26.1                | 231.2                |
+| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024                | 81.3               | 1408.6 ± 7.7                    | 28.6 ± 1.0                           | 58.8                | 519.1                |
+
+- **mAP<sup>test</sup>** 值指的是在 [DOTAv1 测试集](https://captain-whu.github.io/DOTA/dataset.html)上的单模型多尺度性能。<br>通过 `yolo val obb data=DOTAv1.yaml device=0 split=test` 复现结果，并将合并后的结果提交到 [DOTA 评估服务器](https://captain-whu.github.io/DOTA/evaluation.html)。
+- **速度** 指标是在 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例上对 [DOTAv1 val 图像](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10)进行平均测量的。CPU 速度使用 [ONNX](https://onnx.ai/) 导出进行测量。GPU 速度使用 [TensorRT](https://developer.nvidia.com/tensorrt) 导出进行测量。<br>通过 `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu` 复现结果。
+
+</details>
+
+## 🧩 集成
+
+我们与领先 AI 平台的关键集成扩展了 Ultralytics 产品的功能，增强了数据集标注、训练、可视化和模型管理等任务。了解 Ultralytics 如何与 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/)、[Comet ML](https://docs.ultralytics.com/integrations/comet/)、[Roboflow](https://docs.ultralytics.com/integrations/roboflow/) 和 [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 等合作伙伴协作，优化您的 AI 工作流程。��� [Ultralytics 集成](https://docs.ultralytics.com/integrations/)了解更多信息。
+
+<a href="https://docs.ultralytics.com/integrations/" target="_blank">
+    <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
+</a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://www.ultralytics.com/hub">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.ultralytics.com/integrations/weights-biases/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="Weights & Biases logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.ultralytics.com/integrations/comet/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.ultralytics.com/integrations/neural-magic/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
+</div>
+
+|                                              Ultralytics HUB 🌟                                               |                                              Weights & Biases                                               |                                                               Comet                                                                |                                                       Neural Magic                                                       |
+| :-----------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: |
+| 简化 YOLO 工作流程：使用 [Ultralytics HUB](https://hub.ultralytics.com/) 轻松进行标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/) 跟踪实验、超参数和结果。 | 永久免费的 [Comet ML](https://docs.ultralytics.com/integrations/comet/) 让您能够保存 YOLO 模型、恢复训练并交互式地可视化预测结果。 | 使用 [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/)，将 YOLO 推理速度提高多达 6 倍。 |
+
+## 🌟 Ultralytics HUB
+
+通过 [Ultralytics HUB](https://hub.ultralytics.com/) 体验无缝 AI，这是一个集数据可视化、训练 YOLO 模型和部署于一体的平台——无需编码。使用我们尖端的平台和用户友好的 [Ultralytics App](https://www.ultralytics.com/app-install)，轻松将图像转化为可操作的见解，并将您的 AI 愿景变为现实。立即**免费**开始您的旅程！
+
+<a href="https://www.ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
+
+## 🤝 贡献
+
+我们依靠社区协作蓬勃发展！没有像您这样的开发者的贡献，Ultralytics YOLO 就不会成为如今最先进的框架。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing/)开始贡献。我们也欢迎您的反馈——通过完成我们的[调查问卷](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)分享您的体验。非常**感谢** 🙏 每一位贡献者！
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=1280 -->
+
+[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)
+
+我们期待您的贡献，帮助 Ultralytics 生态系统变得更好！
+
+## 📜 许可证
+
+Ultralytics 提供两种许可选项以满足不同需求：
+
+- **AGPL-3.0 许可证**：这种经 [OSI 批准](https://opensource.org/license/agpl-v3)的开源许可证非常适合学生、研究人员和爱好者。它鼓励开放协作和知识共享。有关完整详细信息，请参阅 [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件。
+- **Ultralytics 企业许可证**：专为商业用途设计，此许可证允许将 Ultralytics 软件和 AI 模型无缝集成到商业产品和服务中，绕过 AGPL-3.0 的开源要求���如果您的使用场景涉及商业部署，请通过 [Ultralytics 授权许可](https://www.ultralytics.com/license)与我们联系。
+
+## 📞 联系方式
+
+有关 Ultralytics 软件的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)。如有疑问、讨论和社区支持，请加入我们在 [Discord](https://discord.com/invite/ultralytics)、[Reddit](https://www.reddit.com/r/ultralytics/?rdt=44154) 和 [Ultralytics 社区论坛](https://community.ultralytics.com/)上的活跃社区。我们随时为您提供有关 Ultralytics 的所有帮助！
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>

ultralytics-main/docker/Dockerfile

@@ -0,0 +1,81 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLO single/multi-GPU training and inference
+
+# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:25.02-py3
+FROM pytorch/pytorch:2.9.1-cuda12.8-cudnn9-runtime
+
+# Set environment variables
+# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library"
+# Suppress TensorFlow cuDNN, cuBLAS, and cuFFT Registration Warnings
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1 \
+    MKL_THREADING_LAYER=GNU \
+    OMP_NUM_THREADS=1 \
+    TF_CPP_MIN_LOG_LEVEL=3
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+# gnupg required for Edge TPU install
+# libsm6 required by libqxcb to create QT-based windows for visualization; set 'QT_DEBUG_PLUGINS=1' to test in docker
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    gcc git zip unzip wget curl htop libgl1 libglib2.0-0 gnupg libsm6 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
+    sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Install pip packages
+RUN uv pip install --system -e "." albumentations faster-coco-eval wandb && \
+    # Remove extra build files \
+    rm -rf tmp /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
+
+# Pull and Run with access to all GPUs
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
+
+# Pull and Run with access to GPUs 2 and 3 (inside container CUDA devices will appear as 0 and 1)
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus '"device=2,3"' $t
+
+# Pull and Run with local directory access
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/shared/datasets:/datasets $t
+
+# Kill all
+# sudo docker kill $(sudo docker ps -q)
+
+# Kill all image-based
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/ultralytics:latest)
+
+# DockerHub tag update
+# t=ultralytics/ultralytics:latest tnew=ultralytics/ultralytics:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
+
+# Clean up
+# sudo docker system prune -a --volumes
+
+# Update Ubuntu drivers
+# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
+
+# DDP test
+# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
+
+# GCP VM from Image
+# docker.io/ultralytics/ultralytics:latest

ultralytics-main/docker/Dockerfile-arm64

@@ -0,0 +1,61 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is aarch64-compatible for Apple M1, M2, M3, Raspberry Pi and other ARM architectures
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu with "FROM arm64v8/ubuntu:22.04" (deprecated)
+# Start FROM Debian image for arm64v8 https://hub.docker.com/r/arm64v8/debian (deprecated)
+# Start FROM official arm64v8 Ubuntu 24.04 image https://hub.docker.com/layers/arm64v8/ubuntu/24.04/
+FROM arm64v8/ubuntu:24.04
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
+# gnupg required for Edge TPU install
+RUN apt update && \
+    apt upgrade -y && \
+    apt install -y --no-install-recommends \
+    python3-pip git zip unzip wget curl htop gcc libgl1 libglib2.0-0 gnupg && \
+    apt clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
+    sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Install pip packages, create python symlink, and remove build files
+RUN pip install uv && \
+    uv pip install --system -e ".[export]" --break-system-packages && \
+    # Creates a symbolic link to make 'python' point to 'python3'
+    ln -sf /usr/bin/python3 /usr/bin/python && \
+    # Remove extra build files
+    rm -rf /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-arm64 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && 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)"/shared/datasets:/datasets $t

ultralytics-main/docker/Dockerfile-conda

@@ -0,0 +1,46 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-conda image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is optimized for Ultralytics Anaconda (https://anaconda.org/conda-forge/ultralytics) installation and usage
+
+# Start FROM miniconda3 image https://hub.docker.com/r/continuumio/miniconda3
+FROM continuumio/miniconda3:latest
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages and conda packages
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends libgl1 && \
+    # Install conda packages
+    # mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory'
+    conda config --set solver libmamba && \
+    conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia && \
+    conda install -c conda-forge ultralytics mkl && \
+    # Remove extra build files
+    rm -rf /var/lib/apt/lists/* /root/.config/Ultralytics/persistent_cache.json
+
+# Copy model
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-conda -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-conda && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t

ultralytics-main/docker/Dockerfile-cpu

@@ -0,0 +1,24 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Lightweight CPU image optimized for inference (extends latest-python)
+
+# Build from Ultralytics Python image
+FROM ultralytics/ultralytics:latest-python
+
+# Set default command to bash
+CMD ["/bin/bash"]
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-cpu && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-cpu && sudo docker run -it --ipc=host --name NAME $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host --name NAME $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)"/shared/datasets:/datasets $t

ultralytics-main/docker/Dockerfile-export

@@ -0,0 +1,16 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Export-optimized derivative of ultralytics/ultralytics:latest for testing and benchmarks
+# Includes all export format dependencies and pre-installed export packages
+
+FROM ultralytics/ultralytics:latest
+
+# Install export dependencies and run exports to AutoInstall packages
+# Numpy 1.26.4 required due to TF export bug with torch 2.8
+# Note tensorrt installed on-demand as depends on runtime environment CUDA version
+RUN uv pip install --system -e ".[export]" "onnxruntime-gpu" paddlepaddle x2paddle numpy==1.26.4 && \
+    # Run exports to AutoInstall packages \
+    yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 && \
+    yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32 && \
+    # Remove temporary files \
+    rm -rf tmp /root/.config/Ultralytics/persistent_cache.json

ultralytics-main/docker/Dockerfile-jetson-jetpack4

@@ -0,0 +1,73 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack4.x for YOLO on Jetson Nano, TX2, Xavier NX, AGX Xavier
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda
+FROM nvcr.io/nvidia/l4t-cuda:10.2.460-runtime
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Add NVIDIA repositories for TensorRT dependencies
+RUN wget -q -O - https://repo.download.nvidia.com/jetson/jetson-ota-public.asc | apt-key add - && \
+  echo "deb https://repo.download.nvidia.com/jetson/common r32.7 main" > /etc/apt/sources.list.d/nvidia-l4t-apt-source.list && \
+  echo "deb https://repo.download.nvidia.com/jetson/t194 r32.7 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+
+# Install dependencies
+# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
+# gnupg required for Edge TPU install
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create symbolic links for python3.8 and pip3
+RUN ln -sf /usr/bin/python3.8 /usr/bin/python3 && \
+    ln -s /usr/bin/pip3 /usr/bin/pip
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
+    sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Replace pyproject.toml TF.js version with 'tensorflowjs>=3.9.0' for JetPack4 compatibility
+RUN sed -i 's/^\( *"tensorflowjs\)>=.*\(".*\)/\1>=3.9.0\2/' pyproject.toml
+
+# Install pip packages (pip must be upgraded first before installing uv due to missing setuptools)
+RUN python3 -m pip install --upgrade pip && \
+    python3 -m pip install uv
+# Install pip packages and remove extra build files
+# Onnxruntime and TensorRT from https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
+RUN uv pip install --system \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl && \
+    uv pip install --system -e ".[export]" && \
+    # Remove extra build files
+    rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack4 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

ultralytics-main/docker/Dockerfile-jetson-jetpack5

@@ -0,0 +1,60 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-jetson-jetpack5 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack5.1.2 for YOLO on Jetson Xavier NX, AGX Xavier, AGX Orin, Orin Nano and Orin NX
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack
+FROM nvcr.io/nvidia/l4t-jetpack:r35.4.1
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    git python3-pip libopenmpi-dev libopenblas-base libomp-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
+    sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Replace pyproject.toml TF.js version with 'tensorflowjs>=3.9.0' for JetPack5 compatibility and install packages
+RUN sed -i 's/^\( *"tensorflowjs\)>=.*\(".*\)/\1>=3.9.0\2/' pyproject.toml && \
+    python3 -m pip install --upgrade pip uv
+
+# Pip install onnxruntime-gpu, torch, torchvision and ultralytics, then remove build files
+RUN uv pip install --system \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.18.0-cp38-cp38-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.2.0-cp38-cp38-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.17.2+c1d70fe-cp38-cp38-linux_aarch64.whl && \
+    uv pip install --system -e ".[export]" && \
+    # Remove extra build files
+    rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack5 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

ultralytics-main/docker/Dockerfile-jetson-jetpack6

@@ -0,0 +1,60 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-jetson-jetpack6 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack6.1 for YOLO on Jetson AGX Orin, Orin NX and Orin Nano Series
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack
+FROM nvcr.io/nvidia/l4t-jetpack:r36.4.0
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install dependencies and cleanup
+ADD https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb .
+RUN dpkg -i cuda-keyring_1.1-1_all.deb && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends \
+    git python3-pip libopenmpi-dev libopenblas-base libomp-dev libcusparselt0 libcusparselt-dev && \
+    rm -rf /var/lib/apt/lists/* cuda-keyring_1.1-1_all.deb
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
+    sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Pip install numpy, onnxruntime-gpu, torch, torchvision and ultralytics, then remove build files
+RUN python3 -m pip install --upgrade pip uv && \
+    uv pip install --system \
+    numpy==1.26.4 \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.20.0-cp310-cp310-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.5.0a0+872d972e41.nv24.08-cp310-cp310-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.20.0a0+afc54f7-cp310-cp310-linux_aarch64.whl && \
+    uv pip install --system -e ".[export]" && \
+    # Remove extra build files
+    rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack6 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

ultralytics-main/docker/Dockerfile-jupyter

@@ -0,0 +1,32 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-jupyter image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image provides JupyterLab interface for interactive YOLO development and includes tutorial notebooks
+
+# Start from Python-based Ultralytics image for full Python environment
+FROM ultralytics/ultralytics:latest-python
+
+# Install JupyterLab for interactive development
+RUN uv pip install --system jupyterlab && \
+    # Create persistent data directory structure
+    mkdir -p /data/{datasets,weights,runs} && \
+    # Configure YOLO directories
+    yolo settings datasets_dir="/data/datasets" weights_dir="/data/weights" runs_dir="/data/runs" && \
+    rm -rf tmp /root/.config/Ultralytics/persistent_cache.json
+
+# Start JupyterLab with tutorial notebook
+ENTRYPOINT ["/usr/local/bin/jupyter", "lab", "--allow-root", "--ip=*", "/ultralytics/examples/tutorial.ipynb"]
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker build -f docker/Dockerfile-jupyter -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker run -it --ipc=host -p 8888:8888 $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker pull $t && sudo docker run -it --ipc=host -p 8888:8888 $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker pull $t && sudo docker run -it --ipc=host -p 8888:8888 -v "$(pwd)"/datasets:/data/datasets $t

ultralytics-main/docker/Dockerfile-python

@@ -0,0 +1,54 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-python image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Lightweight CPU image optimized for YOLO inference
+
+# Use official Python base image for reproducibility (3.11.10 for export and 3.12.10 for inference)
+FROM python:3.11.10-slim-bookworm
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
+    sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Install pip packages
+RUN pip install uv && \
+    uv pip install --system -e . --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match && \
+    # Remove extra build files
+    rm -rf tmp ~/.cache /root/.config/Ultralytics/persistent_cache.json
+
+# 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
+# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && 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)"/shared/datasets:/datasets $t

ultralytics-main/docker/Dockerfile-python-export

@@ -0,0 +1,43 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds ultralytics/ultralytics:latest-python-export image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Full-featured image with export capabilities for YOLO model conversion
+
+# Build from lightweight Ultralytics Python image
+FROM ultralytics/ultralytics:latest-python
+
+# Install export-specific system packages
+# gnupg required for Edge TPU install
+# Java runtime environment (default-jre-headless) required for Sony IMX export
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends gnupg default-jre-headless && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install export dependencies and run exports to AutoInstall packages
+# Prevent torch upgrade to 2.9.0 which has issues with imx export
+RUN sed -i -e 's/"torch>=1.8.0/"torch>=1.8.0,<=2.8.0/' pyproject.toml
+RUN uv pip install --system -e ".[export]" && \
+    # Need lower version of 'numpy' for Sony IMX export
+    uv pip install --system numpy==1.26.4 && \
+    # Run exports to AutoInstall packages
+    yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 && \
+    yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32 && \
+    yolo export model=tmp/yolo11n.pt format=imx imgsz=32 && \
+    uv pip install --system paddlepaddle x2paddle && \
+    # Remove extra build files
+    rm -rf tmp /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-python-export && sudo docker build -f docker/Dockerfile-python-export -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-python-export && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-python-export && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-python-export && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t

ultralytics-main/docker/Dockerfile-runner

@@ -0,0 +1,36 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLO single/multi-GPU training and inference tests
+
+# Start FROM Ultralytics GPU image
+FROM ultralytics/ultralytics:latest
+
+# Set additional environment variables for runner
+ARG RUNNER_VERSION=2.329.0
+ENV RUNNER_ALLOW_RUNASROOT=1 \
+    DEBIAN_FRONTEND=noninteractive
+
+# Set the working directory
+WORKDIR /actions-runner
+
+# Download and unpack the latest runner from https://github.com/actions/runner and install dependencies
+RUN FILENAME=actions-runner-linux-x64-${RUNNER_VERSION}.tar.gz && \
+    curl -o "$FILENAME" -L "https://github.com/actions/runner/releases/download/v${RUNNER_VERSION}/${FILENAME}" && \
+    tar xzf "$FILENAME" && \
+    rm "$FILENAME" && \
+    # Install runner dependencies \
+    uv pip install --system pytest-cov && \
+    ./bin/installdependencies.sh && \
+    apt-get -y install libicu-dev
+
+# JSON ENTRYPOINT command to configure and start runner with default TOKEN and NAME
+ENTRYPOINT ["sh", "-c", "./config.sh --url https://github.com/ultralytics/ultralytics --token ${GITHUB_RUNNER_TOKEN:-TOKEN} --name ${GITHUB_RUNNER_NAME:-NAME} --labels gpu-latest --replace && ./run.sh"]
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-runner && sudo docker build -f docker/Dockerfile-runner -t $t . && sudo docker push $t
+
+# Pull and Run in detached mode with access to GPUs 0 and 1
+# t=ultralytics/ultralytics:latest-runner && sudo docker run -d -e GITHUB_RUNNER_TOKEN=TOKEN -e GITHUB_RUNNER_NAME=NAME --ipc=host --gpus '"device=0,1"' $t

ultralytics-main/docs/README.md

@@ -0,0 +1,145 @@
+<a href="https://www.ultralytics.com/" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
+
+# 📚 Ultralytics Docs
+
+Welcome to Ultralytics Docs, your comprehensive resource for understanding and utilizing our state-of-the-art [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) tools and models, including [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/). These documents are actively maintained and deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com/) for easy access.
+
+[![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)
+[![Check Broken links](https://github.com/ultralytics/docs/actions/workflows/links.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/links.yml)
+[![Check Domains](https://github.com/ultralytics/docs/actions/workflows/check_domains.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/check_domains.yml)
+[![Ultralytics Actions](https://github.com/ultralytics/docs/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/format.yml)
+
+<a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
+
+## 🛠️ Installation
+
+[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/)
+[![Downloads](https://static.pepy.tech/badge/ultralytics)](https://clickpy.clickhouse.com/dashboard/ultralytics)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)
+
+To install the `ultralytics` package in developer mode, which allows you to modify the source code directly, ensure you have [Git](https://git-scm.com/) and [Python](https://www.python.org/) 3.8 or later installed on your system. Then, follow these steps:
+
+1.  Clone the `ultralytics` repository to your local machine using Git:
+
+    ```bash
+    git clone https://github.com/ultralytics/ultralytics.git
+    ```
+
+2.  Navigate to the cloned repository's root directory:
+
+    ```bash
+    cd ultralytics
+    ```
+
+3.  Install the package in editable mode (`-e`) along with its development dependencies (`[dev]`) using [pip](https://pip.pypa.io/en/stable/):
+
+    ```bash
+    pip install -e '.[dev]'
+    ```
+
+    This command installs the `ultralytics` package such that changes to the source code are immediately reflected in your environment, ideal for development.
+
+## 🚀 Building and Serving Locally
+
+The `mkdocs serve` command builds and serves a local version of your [MkDocs](https://www.mkdocs.org/) documentation. This is highly useful during development and testing to preview changes.
+
+```bash
+mkdocs serve
+```
+
+- **Command Breakdown:**
+    - `mkdocs`: The main MkDocs command-line interface tool.
+    - `serve`: The subcommand used to build and locally serve your documentation site.
+- **Note:**
+    - `mkdocs serve` includes live reloading, automatically updating the preview in your browser as you save changes to the documentation files.
+    - To stop the local server, simply press `CTRL+C` in your terminal.
+
+## 🌍 Building and Serving Multi-Language
+
+If your documentation supports multiple languages, follow these steps to build and preview all versions:
+
+1.  Stage all new or modified language Markdown (`.md`) files using Git:
+
+    ```bash
+    git add docs/**/*.md -f
+    ```
+
+2.  Build all language versions into the `/site` directory. This script ensures that relevant root-level files are included and clears the previous build:
+
+    ```bash
+    # Clear existing /site directory to prevent conflicts
+    rm -rf site
+
+    # Build the default language site using the primary config file
+    mkdocs build -f docs/mkdocs.yml
+
+    # Loop through each language-specific config file and build its site
+    for file in docs/mkdocs_*.yml; do
+      echo "Building MkDocs site with $file"
+      mkdocs build -f "$file"
+    done
+    ```
+
+3.  To preview the complete multi-language site locally, navigate into the build output directory and start a simple [Python HTTP server](https://docs.python.org/3/library/http.server.html):
+    ```bash
+    cd site
+    python -m http.server
+    # Open http://localhost:8000 in your preferred web browser
+    ```
+    Access the live preview site at `http://localhost:8000`.
+
+## 📤 Deploying Your Documentation Site
+
+To deploy your MkDocs documentation site, choose a hosting provider and configure your deployment method. Common options include [GitHub Pages](https://pages.github.com/), GitLab Pages, or other static site hosting services.
+
+- Configure deployment settings within your `mkdocs.yml` file.
+- Use your hosting provider's recommended workflow (for example running `mkdocs build` in CI or `mkdocs gh-deploy` for GitHub Pages) to publish the generated `site/` directory.
+
+* **GitHub Pages Deployment Example:**
+  If deploying to GitHub Pages, you can use the built-in command:
+
+    ```bash
+    mkdocs gh-deploy
+    ```
+
+    After deployment, you might need to update the "Custom domain" settings in your repository's settings page if you wish to use a personalized URL.
+
+    ![GitHub Pages Custom Domain Setting](https://github.com/ultralytics/docs/releases/download/0/github-pages-custom-domain-setting.avif)
+
+- For detailed instructions on various deployment methods, consult the official [MkDocs Deploying your docs guide](https://www.mkdocs.org/user-guide/deploying-your-docs/).
+
+## 💡 Contribute
+
+We deeply value contributions from the open-source community to enhance Ultralytics projects. Your input helps drive innovation! Please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for detailed information on how to get involved. You can also share your feedback and ideas through our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 to all our contributors for their dedication and support!
+
+![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)
+
+We look forward to your contributions!
+
+## 📜 License
+
+Ultralytics Docs are available under two licensing options to accommodate different usage scenarios:
+
+- **AGPL-3.0 License**: Ideal for students, researchers, and enthusiasts involved in academic pursuits and open collaboration. See the [LICENSE](https://github.com/ultralytics/docs/blob/main/LICENSE) file for full details. This license promotes sharing improvements back with the community.
+- **Enterprise License**: Designed for commercial applications, this license allows seamless integration of Ultralytics software and [AI models](https://docs.ultralytics.com/models/) into commercial products and services. Visit [Ultralytics Licensing](https://www.ultralytics.com/license) for more information on obtaining an Enterprise License.
+
+## ✉️ Contact
+
+For bug reports, feature requests, and other issues related to the documentation, please use [GitHub Issues](https://github.com/ultralytics/docs/issues). For discussions, questions, and community support, join the conversation with peers and the Ultralytics team on our [Discord server](https://discord.com/invite/ultralytics)!
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>

ultralytics-main/docs/build_docs.py

@@ -0,0 +1,691 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Automates building and post-processing of MkDocs documentation, especially for multilingual projects.
+
+This script streamlines generating localized documentation and updating HTML links for correct formatting.
+
+Key Features:
+    - Automated building of MkDocs documentation: Compiles main documentation and localized versions from separate
+      MkDocs configuration files.
+    - Post-processing of generated HTML files: Updates HTML files to remove '.md' from internal links, ensuring
+      correct navigation in web-based documentation.
+
+Usage:
+    - Run from the root directory of your MkDocs project.
+    - Ensure MkDocs is installed and configuration files (main and localized) are present.
+    - The script builds documentation using MkDocs, then scans HTML files in 'site' to update links.
+    - Ideal for projects with Markdown documentation served as a static website.
+
+Note:
+    - Requires Python and MkDocs to be installed and configured.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+import shutil
+import subprocess
+import tempfile
+import time
+from pathlib import Path
+
+import yaml
+from bs4 import BeautifulSoup
+from minijinja import Environment, load_from_path
+
+try:
+    from plugin import postprocess_site  # mkdocs-ultralytics-plugin
+except ImportError:
+    postprocess_site = None
+
+from build_reference import build_reference_docs, build_reference_for
+
+from ultralytics.utils import LINUX, LOGGER, MACOS
+from ultralytics.utils.tqdm import TQDM
+
+os.environ["JUPYTER_PLATFORM_DIRS"] = "1"  # fix DeprecationWarning: Jupyter is migrating to use standard platformdirs
+DOCS = Path(__file__).parent.resolve()
+SITE = DOCS.parent / "site"
+LINK_PATTERN = re.compile(r"(https?://[^\s()<>]*[^\s()<>.,:;!?\'\"])")
+TITLE_PATTERN = re.compile(r"<title>(.*?)</title>", flags=re.IGNORECASE | re.DOTALL)
+MD_LINK_PATTERN = re.compile(r'(["\']?)([^"\'>\s]+?)\.md(["\']?)')
+DOC_KIND_LABELS = {"Class", "Function", "Method", "Property"}
+DOC_KIND_COLORS = {
+    "Class": "#039dfc",  # blue
+    "Method": "#ef5eff",  # magenta
+    "Function": "#fc9803",  # orange
+    "Property": "#02e835",  # green
+}
+
+
+def prepare_docs_markdown(clone_repos: bool = True):
+    """Build docs using mkdocs."""
+    LOGGER.info("Removing existing build artifacts")
+    shutil.rmtree(SITE, ignore_errors=True)
+    shutil.rmtree(DOCS / "repos", ignore_errors=True)
+
+    if clone_repos:
+        # Get hub-sdk repo
+        repo = "https://github.com/ultralytics/hub-sdk"
+        local_dir = DOCS / "repos" / Path(repo).name
+        subprocess.run(
+            ["git", "clone", "-q", "--depth=1", "--single-branch", "-b", "main", repo, str(local_dir)], check=True
+        )
+        shutil.rmtree(DOCS / "en/hub/sdk", ignore_errors=True)  # delete if exists
+        shutil.copytree(local_dir / "docs", DOCS / "en/hub/sdk")  # for docs
+        LOGGER.info(f"Cloned/Updated {repo} in {local_dir}")
+
+        # Get docs repo
+        repo = "https://github.com/ultralytics/docs"
+        local_dir = DOCS / "repos" / Path(repo).name
+        subprocess.run(
+            ["git", "clone", "-q", "--depth=1", "--single-branch", "-b", "main", repo, str(local_dir)], check=True
+        )
+        shutil.rmtree(DOCS / "en/compare", ignore_errors=True)  # delete if exists
+        shutil.copytree(local_dir / "docs/en/compare", DOCS / "en/compare")  # for docs
+        LOGGER.info(f"Cloned/Updated {repo} in {local_dir}")
+
+    # Add frontmatter
+    for file in TQDM((DOCS / "en").rglob("*.md"), desc="Adding frontmatter"):
+        update_markdown_files(file)
+
+
+def update_markdown_files(md_filepath: Path):
+    """Create or update a Markdown file, ensuring frontmatter is present."""
+    if md_filepath.exists():
+        content = md_filepath.read_text().strip()
+
+        # Replace apostrophes
+        content = content.replace("‘", "'").replace("’", "'")
+
+        # Add frontmatter if missing
+        if not content.strip().startswith("---\n"):
+            header = "---\ncomments: true\ndescription: TODO ADD DESCRIPTION\nkeywords: TODO ADD KEYWORDS\n---\n\n"
+            content = header + content
+
+        # Ensure MkDocs admonitions "=== " lines are preceded and followed by empty newlines
+        lines = content.split("\n")
+        new_lines = []
+        for i, line in enumerate(lines):
+            stripped_line = line.strip()
+            if stripped_line.startswith("=== "):
+                if i > 0 and new_lines[-1] != "":
+                    new_lines.append("")
+                new_lines.append(line)
+                if i < len(lines) - 1 and lines[i + 1].strip() != "":
+                    new_lines.append("")
+            else:
+                new_lines.append(line)
+        content = "\n".join(new_lines)
+
+        # Add EOF newline if missing
+        if not content.endswith("\n"):
+            content += "\n"
+
+        # Save page
+        md_filepath.write_text(content)
+    return
+
+
+def update_docs_html():
+    """Update titles, edit links, and convert plaintext links in HTML documentation in one pass."""
+    from concurrent.futures import ProcessPoolExecutor
+
+    html_files = list(SITE.rglob("*.html"))
+    if not html_files:
+        LOGGER.info("Updated HTML files: 0")
+        return
+    desc = f"Updating HTML at {SITE}"
+    max_workers = os.cpu_count() or 1
+    with ProcessPoolExecutor(max_workers=max_workers) as executor:
+        pbar = TQDM(executor.map(_process_html_file, html_files), total=len(html_files), desc=desc)
+        updated = 0
+        for res in pbar:
+            updated += bool(res)
+            pbar.set_description(f"{desc} ({updated}/{len(html_files)} updated)")
+
+
+def _process_html_file(html_file: Path) -> bool:
+    """Process a single HTML file; returns True if modified."""
+    try:
+        content = html_file.read_text(encoding="utf-8")
+    except Exception as e:
+        LOGGER.warning(f"Could not read {html_file}: {e}")
+        return False
+
+    changed = False
+    try:
+        rel_path = html_file.relative_to(SITE).as_posix()
+    except ValueError:
+        rel_path = html_file.name
+
+    # For pages sourced from external repos (hub-sdk, compare), drop edit/copy buttons to avoid wrong links
+    if rel_path.startswith(("hub/sdk/", "compare/")):
+        before = content
+        content = re.sub(
+            r'<a[^>]*class="[^"]*md-content__button[^"]*"[^>]*>.*?</a>',
+            "",
+            content,
+            flags=re.IGNORECASE | re.DOTALL,
+        )
+        if content != before:
+            changed = True
+
+    if rel_path == "404.html":
+        new_content = re.sub(r"<title>.*?</title>", "<title>Ultralytics Docs - Not Found</title>", content)
+        if new_content != content:
+            content, changed = new_content, True
+
+    new_content = update_docs_soup(content, html_file=html_file)
+    if new_content != content:
+        content, changed = new_content, True
+
+    new_content = _rewrite_md_links(content)
+    if new_content != content:
+        content, changed = new_content, True
+
+    if changed:
+        try:
+            html_file.write_text(content, encoding="utf-8")
+            return True
+        except Exception as e:
+            LOGGER.warning(f"Could not write {html_file}: {e}")
+    return False
+
+
+def update_docs_soup(content: str, html_file: Path | None = None, max_title_length: int = 70) -> str:
+    """Convert plaintext links to HTML hyperlinks, truncate long meta titles, and remove code line hrefs."""
+    title_match = TITLE_PATTERN.search(content)
+    needs_title_trim = bool(
+        title_match and len(title_match.group(1)) > max_title_length and "-" in title_match.group(1)
+    )
+    needs_link_conversion = ("<p" in content or "<li" in content) and bool(LINK_PATTERN.search(content))
+    needs_codelineno_cleanup = "__codelineno-" in content
+    rel_path = ""
+    if html_file:
+        try:
+            rel_path = html_file.relative_to(SITE).as_posix()
+        except Exception:
+            rel_path = html_file.as_posix()
+    needs_kind_highlight = "reference" in rel_path or "reference" in content
+
+    if not (needs_title_trim or needs_link_conversion or needs_codelineno_cleanup or needs_kind_highlight):
+        return content
+
+    try:
+        soup = BeautifulSoup(content, "lxml")
+    except Exception:
+        soup = BeautifulSoup(content, "html.parser")
+    modified = False
+
+    # Truncate long meta title if needed
+    title_tag = soup.find("title") if needs_title_trim else None
+    if title_tag and len(title_tag.text) > max_title_length and "-" in title_tag.text:
+        title_tag.string = title_tag.text.rsplit("-", 1)[0].strip()
+        modified = True
+
+    # Find the main content area
+    main_content = soup.find("main") or soup.find("div", class_="md-content")
+    if not main_content:
+        return str(soup) if modified else content
+
+    # Convert plaintext links to HTML hyperlinks
+    if needs_link_conversion:
+        for paragraph in main_content.select("p, li"):
+            for text_node in paragraph.find_all(string=True, recursive=False):
+                if text_node.parent.name not in {"a", "code"}:
+                    new_text = LINK_PATTERN.sub(r'<a href="\1">\1</a>', str(text_node))
+                    if "<a href=" in new_text:
+                        text_node.replace_with(BeautifulSoup(new_text, "html.parser"))
+                        modified = True
+
+    # Remove href attributes from code line numbers in code blocks
+    if needs_codelineno_cleanup:
+        for a in soup.select('a[href^="#__codelineno-"], a[id^="__codelineno-"]'):
+            if a.string:  # If the a tag has text (the line number)
+                # Check if parent is a span with class="normal"
+                if a.parent and a.parent.name == "span" and "normal" in a.parent.get("class", []):
+                    del a.parent["class"]
+                a.replace_with(a.string)  # Replace with just the text
+            else:  # If it has no text
+                a.replace_with(soup.new_tag("span"))  # Replace with an empty span
+            modified = True
+
+    def highlight_labels(nodes):
+        """Inject doc-kind badges into headings and nav entries."""
+        nonlocal modified
+
+        for node in nodes:
+            if not node.contents:
+                continue
+            first = node.contents[0]
+            if hasattr(first, "get") and "doc-kind" in (first.get("class") or []):
+                continue
+            text = first if isinstance(first, str) else getattr(first, "string", "")
+            if not text:
+                continue
+            stripped = str(text).strip()
+            if not stripped:
+                continue
+            kind = stripped.split()[0].rstrip(":")
+            if kind not in DOC_KIND_LABELS:
+                continue
+            span = soup.new_tag("span", attrs={"class": f"doc-kind doc-kind-{kind.lower()}"})
+            span.string = kind.lower()
+            first.replace_with(span)
+            tail = str(text)[len(kind) :]
+            tail_stripped = tail.lstrip()
+            if tail_stripped.startswith(kind):
+                tail = tail_stripped[len(kind) :]
+            if not tail and len(node.contents) > 0:
+                tail = " "
+            if tail:
+                span.insert_after(tail)
+            modified = True
+
+    highlight_labels(soup.select("main h1, main h2, main h3, main h4, main h5"))
+    highlight_labels(soup.select("nav.md-nav--secondary .md-ellipsis, nav.md-nav__list .md-ellipsis"))
+
+    if "reference" in rel_path:
+        for ellipsis in soup.select("nav.md-nav--secondary .md-ellipsis"):
+            kind = ellipsis.find(class_=lambda c: c and "doc-kind" in c.split())
+            text = str(kind.next_sibling).strip() if kind and kind.next_sibling else ellipsis.get_text(strip=True)
+            if "." not in text:
+                continue
+            ellipsis.clear()
+            short = text.rsplit(".", 1)[-1]
+            if kind:
+                ellipsis.append(kind)
+                ellipsis.append(f" {short}")
+            else:
+                ellipsis.append(short)
+            modified = True
+
+    if needs_kind_highlight and not modified and soup.select(".doc-kind"):
+        # Ensure style injection when pre-existing badges are present
+        modified = True
+
+    if modified:
+        head = soup.find("head")
+        if head and not soup.select("style[data-doc-kind]"):
+            style = soup.new_tag("style", attrs={"data-doc-kind": "true"})
+            style.string = (
+                ".doc-kind{display:inline-flex;align-items:center;gap:0.25em;padding:0.21em 0.59em;border-radius:999px;"
+                "font-weight:700;font-size:0.81em;letter-spacing:0.06em;text-transform:uppercase;"
+                "line-height:1;color:var(--doc-kind-color,#f8fafc);"
+                "background:var(--doc-kind-bg,rgba(255,255,255,0.12));}"
+                f".doc-kind-class{{--doc-kind-color:{DOC_KIND_COLORS['Class']};--doc-kind-bg:rgba(3,157,252,0.22);}}"
+                f".doc-kind-function{{--doc-kind-color:{DOC_KIND_COLORS['Function']};--doc-kind-bg:rgba(252,152,3,0.22);}}"
+                f".doc-kind-method{{--doc-kind-color:{DOC_KIND_COLORS['Method']};--doc-kind-bg:rgba(239,94,255,0.22);}}"
+                f".doc-kind-property{{--doc-kind-color:{DOC_KIND_COLORS['Property']};--doc-kind-bg:rgba(2,232,53,0.22);}}"
+            )
+            head.append(style)
+
+    return str(soup) if modified else content
+
+
+def _rewrite_md_links(content: str) -> str:
+    """Replace .md references with trailing slashes in HTML content, skipping GitHub links."""
+    if ".md" not in content:
+        return content
+
+    lines = []
+    for line in content.split("\n"):
+        if "github.com" not in line:
+            line = line.replace("index.md", "")
+            line = MD_LINK_PATTERN.sub(r"\1\2/\3", line)
+        lines.append(line)
+    return "\n".join(lines)
+
+
+# Precompiled regex patterns for minification
+HTML_COMMENT = re.compile(r"<!--[\s\S]*?-->")
+HTML_PRESERVE = re.compile(r"<(pre|code|textarea|script)[^>]*>[\s\S]*?</\1>", re.IGNORECASE)
+HTML_TAG_SPACE = re.compile(r">\s+<")
+HTML_MULTI_SPACE = re.compile(r"\s{2,}")
+HTML_EMPTY_LINE = re.compile(r"^\s*$\n", re.MULTILINE)
+CSS_COMMENT = re.compile(r"/\*[\s\S]*?\*/")
+
+
+def remove_comments_and_empty_lines(content: str, file_type: str) -> str:
+    """Remove comments and empty lines from a string of code, preserving newlines and URLs.
+
+    Args:
+        content (str): Code content to process.
+        file_type (str): Type of file ('html', 'css', or 'js').
+
+    Returns:
+        (str): Cleaned content with comments and empty lines removed.
+
+    Notes:
+        Typical reductions for Ultralytics Docs are:
+        - Total HTML reduction: 2.83% (1301.56 KB saved)
+        - Total CSS reduction: 1.75% (2.61 KB saved)
+        - Total JS reduction: 13.51% (99.31 KB saved)
+    """
+    if file_type == "html":
+        content = HTML_COMMENT.sub("", content)  # Remove HTML comments
+        # Preserve whitespace in <pre>, <code>, <textarea> tags
+        preserved = []
+
+        def preserve(match):
+            """Mark HTML blocks that should not be minified."""
+            preserved.append(match.group(0))
+            return f"___PRESERVE_{len(preserved) - 1}___"
+
+        content = HTML_PRESERVE.sub(preserve, content)
+        content = HTML_TAG_SPACE.sub("><", content)  # Remove whitespace between tags
+        content = HTML_MULTI_SPACE.sub(" ", content)  # Collapse multiple spaces
+        content = HTML_EMPTY_LINE.sub("", content)  # Remove empty lines
+        # Restore preserved content
+        for i, text in enumerate(preserved):
+            content = content.replace(f"___PRESERVE_{i}___", text)
+    elif file_type == "css":
+        content = CSS_COMMENT.sub("", content)  # Remove CSS comments
+        # Remove whitespace around specific characters
+        content = re.sub(r"\s*([{}:;,])\s*", r"\1", content)
+        # Remove empty lines
+        content = re.sub(r"^\s*\n", "", content, flags=re.MULTILINE)
+        # Collapse multiple spaces to single space
+        content = re.sub(r"\s{2,}", " ", content)
+        # Remove all newlines
+        content = re.sub(r"\n", "", content)
+    elif file_type == "js":
+        # Handle JS single-line comments (preserving http:// and https://)
+        lines = content.split("\n")
+        processed_lines = []
+        for line in lines:
+            # Only remove comments if they're not part of a URL
+            if "//" in line and "http://" not in line and "https://" not in line:
+                processed_lines.append(line.partition("//")[0])
+            else:
+                processed_lines.append(line)
+        content = "\n".join(processed_lines)
+
+        # Remove JS multi-line comments and clean whitespace
+        content = re.sub(r"/\*[\s\S]*?\*/", "", content)
+        # Remove empty lines
+        content = re.sub(r"^\s*\n", "", content, flags=re.MULTILINE)
+        # Collapse multiple spaces to single space
+        content = re.sub(r"\s{2,}", " ", content)
+
+        # Safe space removal around punctuation and operators (never include colons - breaks JS)
+        content = re.sub(r"\s*([;{}])\s*", r"\1", content)
+        content = re.sub(r"(\w)\s*\(|\)\s*{|\s*([+\-*/=])\s*", lambda m: m.group(0).replace(" ", ""), content)
+
+    return content
+
+
+def minify_files(html: bool = True, css: bool = True, js: bool = True):
+    """Minify HTML, CSS, and JS files and print total reduction stats."""
+    minify, compress, jsmin = None, None, None
+    try:
+        if html:
+            from minify_html import minify
+        if css:
+            from csscompressor import compress
+        if js:
+            import jsmin
+    except ImportError as e:
+        LOGGER.info(f"Missing required package: {e}")
+        return
+
+    stats = {}
+    for ext, minifier in {
+        "html": (lambda x: minify(x, keep_closing_tags=True, minify_css=True, minify_js=True)) if html else None,
+        "css": compress if css else None,
+        "js": jsmin.jsmin if js else None,
+    }.items():
+        orig = minified = 0
+        files = list(SITE.rglob(f"*.{ext}"))
+        if not files:
+            continue
+        pbar = TQDM(files, desc=f"Minifying {ext.upper()} - reduced 0.00% (0.00 KB saved)")
+        for f in pbar:
+            content = f.read_text(encoding="utf-8")
+            out = minifier(content) if minifier else remove_comments_and_empty_lines(content, ext)
+            orig += len(content)
+            minified += len(out)
+            f.write_text(out, encoding="utf-8")
+            saved = orig - minified
+            pct = (saved / orig) * 100 if orig else 0.0
+            pbar.set_description(f"Minifying {ext.upper()} - reduced {pct:.2f}% ({saved / 1024:.2f} KB saved)")
+        stats[ext] = {"original": orig, "minified": minified}
+
+
+def render_jinja_macros() -> None:
+    """Render MiniJinja macros in markdown files before building with MkDocs."""
+    mkdocs_yml = DOCS.parent / "mkdocs.yml"
+    default_yaml = DOCS.parent / "ultralytics" / "cfg" / "default.yaml"
+
+    class SafeFallbackLoader(yaml.SafeLoader):
+        """SafeLoader that gracefully skips unknown tags (required for mkdocs.yml)."""
+
+    def _ignore_unknown(loader, tag_suffix, node):
+        """Gracefully handle YAML tags that aren't registered."""
+        if isinstance(node, yaml.ScalarNode):
+            return loader.construct_scalar(node)
+        if isinstance(node, yaml.SequenceNode):
+            return loader.construct_sequence(node)
+        if isinstance(node, yaml.MappingNode):
+            return loader.construct_mapping(node)
+        return None
+
+    SafeFallbackLoader.add_multi_constructor("", _ignore_unknown)
+
+    def load_yaml(path: Path, *, safe_loader: yaml.Loader = yaml.SafeLoader) -> dict:
+        """Load YAML safely, returning an empty dict on errors."""
+        if not path.exists():
+            return {}
+        try:
+            with open(path, encoding="utf-8") as f:
+                return yaml.load(f, Loader=safe_loader) or {}
+        except Exception as e:
+            LOGGER.warning(f"Could not load {path}: {e}")
+            return {}
+
+    mkdocs_cfg = load_yaml(mkdocs_yml, safe_loader=SafeFallbackLoader)
+    extra_vars = mkdocs_cfg.get("extra", {}) or {}
+    site_name = mkdocs_cfg.get("site_name", "Ultralytics Docs")
+    extra_vars.update(load_yaml(default_yaml))
+
+    env = Environment(
+        loader=load_from_path([DOCS / "en", DOCS]),
+        auto_escape_callback=lambda _: False,
+        trim_blocks=True,
+        lstrip_blocks=True,
+        keep_trailing_newline=True,
+    )
+
+    def indent_filter(value: str, width: int = 4, first: bool = False, blank: bool = False) -> str:
+        """Mimic Jinja's indent filter to preserve macros compatibility."""
+        prefix = " " * int(width)
+        result = []
+        for i, line in enumerate(str(value).splitlines(keepends=True)):
+            if not line.strip() and not blank:
+                result.append(line)
+                continue
+            if i == 0 and not first:
+                result.append(line)
+            else:
+                result.append(prefix + line)
+        return "".join(result)
+
+    env.add_filter("indent", indent_filter)
+    reserved_keys = {"name"}
+    base_context = {**extra_vars, "page": {"meta": {}}, "config": {"site_name": site_name}}
+
+    files_processed = 0
+    files_with_macros = 0
+    macros_total = 0
+
+    pbar = TQDM((DOCS / "en").rglob("*.md"), desc="MiniJinja: 0 macros, 0 pages")
+    for md_file in pbar:
+        if "macros" in md_file.parts or "reference" in md_file.parts:
+            continue
+        files_processed += 1
+
+        try:
+            content = md_file.read_text(encoding="utf-8")
+        except Exception as e:
+            LOGGER.warning(f"Could not read {md_file}: {e}")
+            continue
+        if "{{" not in content and "{%" not in content:
+            continue
+
+        parts = content.split("---\n")
+        frontmatter = ""
+        frontmatter_data = {}
+        markdown_content = content
+        if content.startswith("---\n") and len(parts) >= 3:
+            frontmatter = f"---\n{parts[1]}---\n"
+            markdown_content = "---\n".join(parts[2:])
+            try:
+                frontmatter_data = yaml.safe_load(parts[1]) or {}
+            except Exception as e:
+                LOGGER.warning(f"Could not parse frontmatter in {md_file}: {e}")
+
+        macro_hits = markdown_content.count("{{") + markdown_content.count("{%")
+        if not macro_hits:
+            continue
+
+        context = {k: v for k, v in base_context.items() if k not in reserved_keys}
+        context.update({k: v for k, v in frontmatter_data.items() if k not in reserved_keys})
+        context["page"] = context.get("page", {})
+        context["page"]["meta"] = frontmatter_data
+
+        try:
+            rendered = env.render_str(markdown_content, name=str(md_file.relative_to(DOCS)), **context)
+        except Exception as e:
+            LOGGER.warning(f"Error rendering macros in {md_file}: {e}")
+            continue
+
+        md_file.write_text(frontmatter + rendered, encoding="utf-8")
+        files_with_macros += 1
+        macros_total += macro_hits
+        pbar.set_description(f"MiniJinja: {macros_total} macros, {files_with_macros} pages")
+
+
+def backup_docs_sources() -> tuple[Path, list[tuple[Path, Path]]]:
+    """Create a temporary backup of docs sources so we can fully restore after building."""
+    backup_root = Path(tempfile.mkdtemp(prefix="docs_backup_", dir=str(DOCS.parent)))
+    sources = [DOCS / "en", DOCS / "macros"]
+    copied: list[tuple[Path, Path]] = []
+    for src in sources:
+        if not src.exists():
+            continue
+        dst = backup_root / src.name
+        shutil.copytree(src, dst)
+        copied.append((src, dst))
+    return backup_root, copied
+
+
+def restore_docs_sources(backup_root: Path, backups: list[tuple[Path, Path]]):
+    """Restore docs sources from the temporary backup."""
+    for src, dst in backups:
+        shutil.rmtree(src, ignore_errors=True)
+        if dst.exists():
+            shutil.copytree(dst, src)
+    shutil.rmtree(backup_root, ignore_errors=True)
+
+
+def main():
+    """Build docs, update titles and edit links, minify HTML, and print local server command."""
+    start_time = time.perf_counter()
+    backup_root: Path | None = None
+    docs_backups: list[tuple[Path, Path]] = []
+    restored = False
+
+    def restore_all():
+        """Restore docs sources from backup once build steps complete."""
+        nonlocal restored
+        if backup_root:
+            LOGGER.info("Restoring docs directory from backup")
+            restore_docs_sources(backup_root, docs_backups)
+        restored = True
+
+    try:
+        backup_root, docs_backups = backup_docs_sources()
+        prepare_docs_markdown()
+        build_reference_docs(update_nav=False)
+        # Render reference docs for any extra packages present (e.g., hub-sdk)
+        extra_refs = [
+            {
+                "package": DOCS / "repos" / "hub-sdk" / "hub_sdk",
+                "reference_dir": DOCS / "en" / "hub" / "sdk" / "reference",
+                "repo": "ultralytics/hub-sdk",
+            },
+        ]
+        for ref in extra_refs:
+            if ref["package"].exists():
+                build_reference_for(ref["package"], ref["reference_dir"], ref["repo"], update_nav=False)
+        render_jinja_macros()
+
+        # Remove cloned repos before serving/building to keep the tree lean during mkdocs processing
+        shutil.rmtree(DOCS / "repos", ignore_errors=True)
+
+        # Build the main documentation
+        LOGGER.info(f"Building docs from {DOCS}")
+        subprocess.run(["zensical", "build", "-f", str(DOCS.parent / "mkdocs.yml")], check=True)
+        LOGGER.info(f"Site built at {SITE}")
+
+        # Update docs HTML pages
+        update_docs_html()
+
+        # Post-process site for meta tags, authors, social cards, and mkdocstrings polish
+        if postprocess_site:
+            postprocess_site(
+                site_dir=SITE,
+                docs_dir=DOCS / "en",
+                site_url="https://docs.ultralytics.com",
+                default_image="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png",
+                default_author="[email protected]",
+                add_desc=False,
+                add_image=True,
+                add_authors=True,
+                add_json_ld=True,
+                add_share_buttons=True,
+                add_css=False,
+                verbose=True,
+            )
+        else:
+            LOGGER.warning("postprocess_site not available; skipping mkdocstrings postprocessing")
+
+        # Minify files
+        minify_files(html=False, css=False, js=False)
+
+        # Print results and auto-serve on macOS
+        size = sum(f.stat().st_size for f in SITE.rglob("*") if f.is_file()) >> 20
+        duration = time.perf_counter() - start_time
+        LOGGER.info(f"Docs built correctly ✅ ({size:.1f}MB, {duration:.1f}s)")
+
+        # Restore sources before optionally serving
+        restore_all()
+
+        if (MACOS or LINUX) and not os.getenv("GITHUB_ACTIONS"):
+            import webbrowser
+
+            url = "http://localhost:8000"
+            LOGGER.info(f"Opening browser at {url}")
+            webbrowser.open(url)
+            try:
+                subprocess.run(["python", "-m", "http.server", "--directory", str(SITE), "8000"], check=True)
+            except KeyboardInterrupt:
+                LOGGER.info(f"\n✅ Server stopped. Restart at {url}")
+            except Exception as e:
+                if "Address already in use" in str(e):
+                    LOGGER.info("Port 8000 in use; skipping auto-serve. Serve manually if needed.")
+                else:
+                    LOGGER.info(f"\n❌ Server failed: {e}")
+        else:
+            LOGGER.info('Serve site at http://localhost:8000 with "python -m http.server --directory site"')
+    finally:
+        if not restored:
+            restore_all()
+        shutil.rmtree(DOCS.parent / "hub_sdk", ignore_errors=True)
+        shutil.rmtree(DOCS / "repos", ignore_errors=True)
+
+
+if __name__ == "__main__":
+    main()

ultralytics-main/docs/build_reference.py

@@ -0,0 +1,1191 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Helper file to build Ultralytics Docs reference section.
+
+This script recursively walks through the ultralytics directory and builds a MkDocs reference section of *.md files
+composed of classes and functions, and also creates a navigation menu for use in mkdocs.yaml.
+
+Note: Must be run from repository root directory. Do not run from docs directory.
+"""
+
+from __future__ import annotations
+
+import ast
+import html
+import re
+import subprocess
+import textwrap
+from collections import defaultdict
+from collections.abc import Iterable
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Literal
+
+from ultralytics.utils.tqdm import TQDM
+
+# Constants
+FILE = Path(__file__).resolve()
+REPO_ROOT = FILE.parents[1]
+PACKAGE_DIR = REPO_ROOT / "ultralytics"
+REFERENCE_DIR = PACKAGE_DIR.parent / "docs/en/reference"
+GITHUB_REPO = "ultralytics/ultralytics"
+SIGNATURE_LINE_LENGTH = 120
+# Use Font Awesome brand GitHub icon (CSS already loaded via mkdocs.yml and HTML head)
+GITHUB_ICON = '<i class="fa-brands fa-github" aria-hidden="true" style="margin-right:6px;"></i>'
+
+MKDOCS_YAML = PACKAGE_DIR.parent / "mkdocs.yml"
+INCLUDE_SPECIAL_METHODS = {
+    "__call__",
+    "__dir__",
+    "__enter__",
+    "__exit__",
+    "__aenter__",
+    "__aexit__",
+    "__getitem__",
+    "__iter__",
+    "__len__",
+    "__next__",
+    "__getattr__",
+}
+PROPERTY_DECORATORS = {"property", "cached_property"}
+CLASS_DEF_RE = re.compile(r"(?:^|\n)class\s(\w+)(?:\(|:)")
+FUNC_DEF_RE = re.compile(r"(?:^|\n)(?:async\s+)?def\s(\w+)\(")
+SECTION_ENTRY_RE = re.compile(r"([\w*]+)\s*(?:\(([^)]+)\))?:\s*(.*)")
+RETURNS_RE = re.compile(r"([^:]+):\s*(.*)")
+
+
+@dataclass
+class ParameterDoc:
+    """Structured documentation for parameters, attributes, and exceptions."""
+
+    name: str
+    type: str | None
+    description: str
+    default: str | None = None
+
+
+@dataclass
+class ReturnDoc:
+    """Structured documentation for return and yield values."""
+
+    type: str | None
+    description: str
+
+
+@dataclass
+class ParsedDocstring:
+    """Normalized representation of a Google-style docstring."""
+
+    summary: str = ""
+    description: str = ""
+    params: list[ParameterDoc] = field(default_factory=list)
+    attributes: list[ParameterDoc] = field(default_factory=list)
+    returns: list[ReturnDoc] = field(default_factory=list)
+    yields: list[ReturnDoc] = field(default_factory=list)
+    raises: list[ParameterDoc] = field(default_factory=list)
+    notes: list[str] = field(default_factory=list)
+    examples: list[str] = field(default_factory=list)
+
+
+@dataclass
+class DocItem:
+    """Represents a documented symbol (class, function, method, or property)."""
+
+    name: str
+    qualname: str
+    kind: Literal["class", "function", "method", "property"]
+    signature: str
+    doc: ParsedDocstring
+    signature_params: list[ParameterDoc]
+    lineno: int
+    end_lineno: int
+    bases: list[str] = field(default_factory=list)
+    children: list[DocItem] = field(default_factory=list)
+    module_path: str = ""
+    source: str = ""
+
+
+@dataclass
+class DocumentedModule:
+    """Container for all documented items within a Python module."""
+
+    path: Path
+    module_path: str
+    classes: list[DocItem]
+    functions: list[DocItem]
+
+
+# --------------------------------------------------------------------------------------------- #
+# Placeholder (legacy) generation for mkdocstrings-style stubs
+# --------------------------------------------------------------------------------------------- #
+
+
+def extract_classes_and_functions(filepath: Path) -> tuple[list[str], list[str]]:
+    """Extract top-level class and (a)sync function names from a Python file."""
+    content = filepath.read_text()
+    classes = CLASS_DEF_RE.findall(content)
+    functions = FUNC_DEF_RE.findall(content)
+    return classes, functions
+
+
+def create_placeholder_markdown(py_filepath: Path, module_path: str, classes: list[str], functions: list[str]) -> Path:
+    """Create a minimal Markdown stub used by mkdocstrings."""
+    md_filepath = REFERENCE_DIR / py_filepath.relative_to(PACKAGE_DIR).with_suffix(".md")
+    exists = md_filepath.exists()
+
+    header_content = ""
+    if exists:
+        current = md_filepath.read_text()
+        if current.startswith("---"):
+            parts = current.split("---", 2)
+            if len(parts) > 2:
+                header_content = f"---{parts[1]}---\n\n"
+    if not header_content:
+        header_content = "---\ndescription: TODO ADD DESCRIPTION\nkeywords: TODO ADD KEYWORDS\n---\n\n"
+
+    module_path_dots = module_path
+    module_path_fs = module_path.replace(".", "/")
+    url = f"https://github.com/{GITHUB_REPO}/blob/main/{module_path_fs}.py"
+    pretty = url.replace("__init__.py", "\\_\\_init\\_\\_.py")
+
+    title_content = f"# Reference for `{module_path_fs}.py`\n\n" + contribution_admonition(
+        pretty, url, kind="success", title="Improvements"
+    )
+
+    md_content = ["<br>\n\n"]
+    md_content.extend(f"## ::: {module_path_dots}.{cls}\n\n<br><br><hr><br>\n\n" for cls in classes)
+    md_content.extend(f"## ::: {module_path_dots}.{func}\n\n<br><br><hr><br>\n\n" for func in functions)
+    if md_content[-1:]:
+        md_content[-1] = md_content[-1].replace("<hr><br>\n\n", "")
+
+    md_filepath.parent.mkdir(parents=True, exist_ok=True)
+    md_filepath.write_text(header_content + title_content + "".join(md_content) + "\n")
+
+    return _relative_to_workspace(md_filepath)
+
+
+def _get_source(src: str, node: ast.AST) -> str:
+    """Return the source segment for an AST node with safe fallbacks."""
+    segment = ast.get_source_segment(src, node)
+    if segment:
+        return segment
+    try:
+        return ast.unparse(node)
+    except Exception:
+        return ""
+
+
+def _format_annotation(annotation: ast.AST | None, src: str) -> str | None:
+    """Format a type annotation into a compact string."""
+    if annotation is None:
+        return None
+    text = _get_source(src, annotation).strip()
+    return " ".join(text.split()) if text else None
+
+
+def _format_default(default: ast.AST | None, src: str) -> str | None:
+    """Format a default value expression for display."""
+    if default is None:
+        return None
+    text = _get_source(src, default).strip()
+    return " ".join(text.split()) if text else None
+
+
+def _format_parameter(arg: ast.arg, default: ast.AST | None, src: str) -> str:
+    """Render a single parameter with annotation and default value."""
+    annotation = _format_annotation(arg.annotation, src)
+    rendered = arg.arg
+    if annotation:
+        rendered += f": {annotation}"
+    default_value = _format_default(default, src)
+    if default_value is not None:
+        rendered += f" = {default_value}"
+    return rendered
+
+
+def collect_signature_parameters(args: ast.arguments, src: str, *, skip_self: bool = True) -> list[ParameterDoc]:
+    """Collect parameters from an ast.arguments object with types and defaults."""
+    params: list[ParameterDoc] = []
+
+    def add_param(arg: ast.arg, default_value: ast.AST | None = None):
+        """Append a parameter entry, optionally skipping self/cls."""
+        name = arg.arg
+        if skip_self and name in {"self", "cls"}:
+            return
+        params.append(
+            ParameterDoc(
+                name=name,
+                type=_format_annotation(arg.annotation, src),
+                description="",
+                default=_format_default(default_value, src),
+            )
+        )
+
+    posonly = list(getattr(args, "posonlyargs", []))
+    regular = list(getattr(args, "args", []))
+    defaults = list(getattr(args, "defaults", []))
+    total_regular = len(posonly) + len(regular)
+    default_offset = total_regular - len(defaults)
+
+    combined = posonly + regular
+    for idx, arg in enumerate(combined):
+        default = defaults[idx - default_offset] if idx >= default_offset else None
+        add_param(arg, default)
+
+    vararg = getattr(args, "vararg", None)
+    if vararg:
+        add_param(vararg)
+        params[-1].name = f"*{params[-1].name}"
+
+    kwonly = list(getattr(args, "kwonlyargs", []))
+    kw_defaults = list(getattr(args, "kw_defaults", []))
+    for kwarg, default in zip(kwonly, kw_defaults):
+        add_param(kwarg, default)
+
+    kwarg = getattr(args, "kwarg", None)
+    if kwarg:
+        add_param(kwarg)
+        params[-1].name = f"**{params[-1].name}"
+
+    return params
+
+
+def format_signature(
+    node: ast.AST, src: str, *, is_class: bool = False, is_async: bool = False, display_name: str | None = None
+) -> str:
+    """Build a readable signature string for classes, functions, and methods."""
+    if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)):
+        return ""
+
+    if isinstance(node, ast.ClassDef):
+        init_method = next(
+            (n for n in node.body if isinstance(n, (ast.FunctionDef, ast.AsyncFunctionDef)) and n.name == "__init__"),
+            None,
+        )
+        args = (
+            init_method.args
+            if init_method
+            else ast.arguments(
+                posonlyargs=[], args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]
+            )
+        )
+    else:
+        args = node.args
+    name = display_name or getattr(node, "name", "")
+    params: list[str] = []
+
+    posonly = list(getattr(args, "posonlyargs", []))
+    regular = list(getattr(args, "args", []))
+    defaults = list(getattr(args, "defaults", []))
+    total_regular = len(posonly) + len(regular)
+    default_offset = total_regular - len(defaults)
+
+    combined = posonly + regular
+    for idx, arg in enumerate(combined):
+        default = defaults[idx - default_offset] if idx >= default_offset else None
+        params.append(_format_parameter(arg, default, src))
+        if posonly and idx == len(posonly) - 1:
+            params.append("/")
+
+    vararg = getattr(args, "vararg", None)
+    if vararg:
+        rendered = _format_parameter(vararg, None, src)
+        params.append(f"*{rendered}")
+
+    kwonly = list(getattr(args, "kwonlyargs", []))
+    kw_defaults = list(getattr(args, "kw_defaults", []))
+    if kwonly:
+        if not vararg:
+            params.append("*")
+        for kwarg, default in zip(kwonly, kw_defaults):
+            params.append(_format_parameter(kwarg, default, src))
+
+    kwarg = getattr(args, "kwarg", None)
+    if kwarg:
+        rendered = _format_parameter(kwarg, None, src)
+        params.append(f"**{rendered}")
+
+    return_annotation = (
+        _format_annotation(node.returns, src)
+        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) and node.returns
+        else None
+    )
+
+    prefix = "" if is_class else ("async def " if is_async else "def ")
+    signature = f"{prefix}{name}({', '.join(params)})"
+    if return_annotation:
+        signature += f" -> {return_annotation}"
+
+    if len(signature) <= SIGNATURE_LINE_LENGTH or not params:
+        return signature
+
+    raw_signature = _get_definition_signature(node, src)
+    return raw_signature or signature
+
+
+def _split_section_entries(lines: list[str]) -> list[list[str]]:
+    """Split a docstring section into entries based on indentation."""
+    entries: list[list[str]] = []
+    current: list[str] = []
+    base_indent: int | None = None
+
+    for raw_line in lines:
+        if not raw_line.strip():
+            if current:
+                current.append("")
+            continue
+        indent = len(raw_line) - len(raw_line.lstrip(" "))
+        if base_indent is None:
+            base_indent = indent
+        if indent <= base_indent and current:
+            entries.append(current)
+            current = [raw_line]
+        else:
+            current.append(raw_line)
+    if current:
+        entries.append(current)
+    return entries
+
+
+def _parse_named_entries(lines: list[str]) -> list[ParameterDoc]:
+    """Parse Args/Attributes/Raises style sections."""
+    entries = []
+    for block in _split_section_entries(lines):
+        text = textwrap.dedent("\n".join(block)).strip()
+        if not text:
+            continue
+        first_line, *rest = text.splitlines()
+        match = SECTION_ENTRY_RE.match(first_line)
+        if match:
+            name, type_hint, desc = match.groups()
+            description = " ".join(desc.split())
+            if rest:
+                description = f"{description}\n" + "\n".join(rest)
+            entries.append(ParameterDoc(name=name, type=type_hint, description=_normalize_text(description)))
+        else:
+            entries.append(ParameterDoc(name=text, type=None, description=""))
+    return entries
+
+
+def _parse_returns(lines: list[str]) -> list[ReturnDoc]:
+    """Parse Returns/Yields sections."""
+    entries = []
+    for block in _split_section_entries(lines):
+        text = textwrap.dedent("\n".join(block)).strip()
+        if not text:
+            continue
+        match = RETURNS_RE.match(text)
+        if match:
+            type_hint, desc = match.groups()
+            cleaned_type = type_hint.strip()
+            if cleaned_type.startswith("(") and cleaned_type.endswith(")"):
+                cleaned_type = cleaned_type[1:-1].strip()
+            entries.append(ReturnDoc(type=cleaned_type, description=_normalize_text(desc.strip())))
+        else:
+            entries.append(ReturnDoc(type=None, description=_normalize_text(text)))
+    return entries
+
+
+SECTION_ALIASES = {
+    "args": "params",
+    "arguments": "params",
+    "parameters": "params",
+    "params": "params",
+    "returns": "returns",
+    "return": "returns",
+    "yields": "yields",
+    "yield": "yields",
+    "raises": "raises",
+    "exceptions": "raises",
+    "exception": "raises",
+    "attributes": "attributes",
+    "attr": "attributes",
+    "examples": "examples",
+    "example": "examples",
+    "notes": "notes",
+    "note": "notes",
+    "methods": "methods",
+}
+
+
+def _normalize_text(text: str) -> str:
+    """Collapse single newlines within paragraphs while preserving paragraph breaks."""
+    if not text:
+        return ""
+    paragraphs: list[str] = []
+    current: list[str] = []
+    for line in text.splitlines():
+        stripped = line.strip()
+        if not stripped:
+            if current:
+                paragraphs.append(" ".join(current))
+                current = []
+            continue
+        current.append(stripped)
+    if current:
+        paragraphs.append(" ".join(current))
+    return "\n\n".join(paragraphs)
+
+
+def parse_google_docstring(docstring: str | None) -> ParsedDocstring:
+    """Parse a Google-style docstring into structured data."""
+    if not docstring:
+        return ParsedDocstring()
+
+    lines = textwrap.dedent(docstring).splitlines()
+    while lines and not lines[0].strip():
+        lines.pop(0)
+    if not lines:
+        return ParsedDocstring()
+
+    summary = _normalize_text(lines[0].strip())
+    body = lines[1:]
+
+    sections: defaultdict[str, list[str]] = defaultdict(list)
+    current = "description"
+    for line in body:
+        stripped = line.strip()
+        key = SECTION_ALIASES.get(stripped.rstrip(":").lower())
+        if key and stripped.endswith(":"):
+            current = key
+            continue
+        if current != "methods":  # ignore "Methods:" sections; methods are rendered from AST
+            sections[current].append(line)
+
+    description = "\n".join(sections.pop("description", [])).strip("\n")
+    description = _normalize_text(description)
+
+    return ParsedDocstring(
+        summary=summary,
+        description=description,
+        params=_parse_named_entries(sections.get("params", [])),
+        attributes=_parse_named_entries(sections.get("attributes", [])),
+        returns=_parse_returns(sections.get("returns", [])),
+        yields=_parse_returns(sections.get("yields", [])),
+        raises=_parse_named_entries(sections.get("raises", [])),
+        notes=[textwrap.dedent("\n".join(sections.get("notes", []))).strip()] if sections.get("notes") else [],
+        examples=[textwrap.dedent("\n".join(sections.get("examples", []))).strip()] if sections.get("examples") else [],
+    )
+
+
+def merge_docstrings(base: ParsedDocstring, extra: ParsedDocstring, ignore_summary: bool = True) -> ParsedDocstring:
+    """Merge init docstring content into a class docstring."""
+
+    # Keep existing class docs; append init docs only when they introduce new entries (class takes priority).
+    def _merge_unique(base_items, extra_items, key):
+        seen = {key(item) for item in base_items}
+        base_items.extend(item for item in extra_items if key(item) not in seen)
+        return base_items
+
+    if not base.summary and extra.summary and not ignore_summary:
+        base.summary = extra.summary
+    if extra.description:
+        base.description = "\n\n".join(filter(None, [base.description, extra.description]))
+    _merge_unique(base.params, extra.params, lambda p: (p.name, p.type, p.description, p.default))
+    _merge_unique(base.attributes, extra.attributes, lambda p: (p.name, p.type, p.description, p.default))
+    _merge_unique(base.returns, extra.returns, lambda r: (r.type, r.description))
+    _merge_unique(base.yields, extra.yields, lambda r: (r.type, r.description))
+    _merge_unique(base.raises, extra.raises, lambda r: (r.name, r.type, r.description, r.default))
+    _merge_unique(base.notes, extra.notes, lambda n: n.strip())
+    _merge_unique(base.examples, extra.examples, lambda e: e.strip())
+    return base
+
+
+def _should_document(name: str, *, allow_private: bool = False) -> bool:
+    """Decide whether to include a symbol based on its name."""
+    if name in INCLUDE_SPECIAL_METHODS:
+        return True
+    if name.startswith("_"):
+        return allow_private
+    return True
+
+
+def _collect_source_block(src: str, node: ast.AST, end_line: int | None = None) -> str:
+    """Return a dedented source snippet for the given node up to an optional end line."""
+    if not hasattr(node, "lineno") or not hasattr(node, "end_lineno"):
+        return ""
+    lines = src.splitlines()
+    # Include decorators by starting from the first decorator line if present
+    decorator_lines = [getattr(d, "lineno", node.lineno) for d in getattr(node, "decorator_list", [])]
+    start_line = min([*decorator_lines, node.lineno]) if decorator_lines else node.lineno
+    start = max(start_line - 1, 0)
+    end = end_line or getattr(node, "end_lineno", node.lineno)
+    snippet = "\n".join(lines[start:end])
+    return textwrap.dedent(snippet).rstrip()
+
+
+def _get_definition_signature(node: ast.AST, src: str) -> str:
+    """Return the original multi-line definition signature from source if available."""
+    if not hasattr(node, "lineno"):
+        return ""
+    lines = src.splitlines()[node.lineno - 1 :]
+    collected: list[str] = []
+    for line in lines:
+        stripped = line.strip()
+        if not stripped:
+            continue
+        collected.append(line)
+        if stripped.endswith(":"):
+            break
+    header = textwrap.dedent("\n".join(collected)).rstrip()
+    return header[:-1].rstrip() if header.endswith(":") else header
+
+
+def parse_function(
+    node: ast.FunctionDef | ast.AsyncFunctionDef,
+    module_path: str,
+    src: str,
+    *,
+    parent: str | None = None,
+    allow_private: bool = False,
+) -> DocItem | None:
+    """Parse a function or method node into a DocItem."""
+    raw_docstring = ast.get_docstring(node)
+    if not _should_document(node.name, allow_private=allow_private) and not raw_docstring:
+        return None
+
+    is_async = isinstance(node, ast.AsyncFunctionDef)
+    doc = parse_google_docstring(raw_docstring)
+    qualname = f"{module_path}.{node.name}" if not parent else f"{parent}.{node.name}"
+    decorators = {_get_source(src, d).split(".")[-1] for d in node.decorator_list}
+    kind: Literal["function", "method", "property"] = "method" if parent else "function"
+    if decorators & PROPERTY_DECORATORS:
+        kind = "property"
+
+    signature_params = collect_signature_parameters(node.args, src, skip_self=bool(parent))
+
+    return DocItem(
+        name=node.name,
+        qualname=qualname,
+        kind=kind,
+        signature=format_signature(node, src, is_async=is_async),
+        doc=doc,
+        signature_params=signature_params,
+        lineno=node.lineno,
+        end_lineno=node.end_lineno or node.lineno,
+        bases=[],
+        children=[],
+        module_path=module_path,
+        source=_collect_source_block(src, node),
+    )
+
+
+def parse_class(node: ast.ClassDef, module_path: str, src: str) -> DocItem:
+    """Parse a class node, merging __init__ docs and collecting methods."""
+    class_doc = parse_google_docstring(ast.get_docstring(node))
+
+    init_node: ast.FunctionDef | ast.AsyncFunctionDef | None = next(
+        (n for n in node.body if isinstance(n, (ast.FunctionDef, ast.AsyncFunctionDef)) and n.name == "__init__"),
+        None,
+    )
+    signature_params: list[ParameterDoc] = []
+    if init_node:
+        init_doc = parse_google_docstring(ast.get_docstring(init_node))
+        class_doc = merge_docstrings(class_doc, init_doc, ignore_summary=True)
+        signature_params = collect_signature_parameters(init_node.args, src, skip_self=True)
+
+    bases = [_get_source(src, b) for b in node.bases] if node.bases else []
+    signature_node = init_node or node
+    class_signature = format_signature(signature_node, src, is_class=True, display_name=node.name)
+
+    methods: list[DocItem] = []
+    for child in node.body:
+        if isinstance(child, (ast.FunctionDef, ast.AsyncFunctionDef)) and child is not init_node:
+            method_doc = parse_function(child, module_path, src, parent=f"{module_path}.{node.name}")
+            if method_doc:
+                methods.append(method_doc)
+
+    return DocItem(
+        name=node.name,
+        qualname=f"{module_path}.{node.name}",
+        kind="class",
+        signature=class_signature,
+        doc=class_doc,
+        signature_params=signature_params,
+        lineno=node.lineno,
+        end_lineno=node.end_lineno or node.lineno,
+        bases=bases,
+        children=methods,
+        module_path=module_path,
+        source=_collect_source_block(src, node, end_line=init_node.end_lineno if init_node else node.lineno),
+    )
+
+
+def parse_module(py_filepath: Path) -> DocumentedModule | None:
+    """Parse a Python module into structured documentation objects."""
+    try:
+        src = py_filepath.read_text(encoding="utf-8")
+    except Exception:
+        return None
+    try:
+        tree = ast.parse(src)
+    except SyntaxError:
+        return None
+
+    module_path = (
+        f"{PACKAGE_DIR.name}.{py_filepath.relative_to(PACKAGE_DIR).with_suffix('').as_posix().replace('/', '.')}"
+    )
+    classes: list[DocItem] = []
+    functions: list[DocItem] = []
+
+    for node in tree.body:
+        if isinstance(node, ast.ClassDef):
+            classes.append(parse_class(node, module_path, src))
+        elif isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+            func = parse_function(node, module_path, src, parent=None)
+            if func:
+                functions.append(func)
+
+    return DocumentedModule(path=py_filepath, module_path=module_path, classes=classes, functions=functions)
+
+
+def _render_section(title: str, entries: Iterable[str], level: int) -> str:
+    """Render a section with a given heading level."""
+    entries = list(entries)
+    if not entries:
+        return ""
+    heading = f"{'#' * level} {title}\n"
+    body = "\n".join(entries).rstrip()
+    return f"{heading}{body}\n\n"
+
+
+def _render_table(headers: list[str], rows: list[list[str]], level: int, title: str | None = None) -> str:
+    """Render a Markdown table with an optional heading."""
+    if not rows:
+        return ""
+
+    def _clean_cell(value: str | None) -> str:
+        """Normalize table cell values for Markdown output."""
+        if value is None:
+            return ""
+        return str(value).replace("\n", "<br>").strip()
+
+    rows = [[_clean_cell(c) for c in row] for row in rows]
+    table_lines = ["| " + " | ".join(headers) + " |", "| " + " | ".join("---" for _ in headers) + " |"]
+    for row in rows:
+        table_lines.append("| " + " | ".join(row) + " |")
+    heading = f"{'#' * level} {title}\n" if title else ""
+    return f"{heading}" + "\n".join(table_lines) + "\n\n"
+
+
+def _code_fence(source: str, lang: str = "python") -> str:
+    """Return a fenced code block with optional language for highlighting."""
+    return f"```{lang}\n{source}\n```"
+
+
+def _merge_params(doc_params: list[ParameterDoc], signature_params: list[ParameterDoc]) -> list[ParameterDoc]:
+    """Merge docstring params with signature params to include defaults/types."""
+    sig_map = {p.name.lstrip("*"): p for p in signature_params}
+    merged: list[ParameterDoc] = []
+
+    seen = set()
+    for dp in doc_params:
+        sig = sig_map.get(dp.name.lstrip("*"))
+        merged.append(
+            ParameterDoc(
+                name=dp.name,
+                type=dp.type or (sig.type if sig else None),
+                description=dp.description,
+                default=sig.default if sig else None,
+            )
+        )
+        seen.add(dp.name.lstrip("*"))
+
+    for name, sig in sig_map.items():
+        if name in seen:
+            continue
+        merged.append(sig)
+
+    return merged
+
+
+DEFAULT_SECTION_ORDER = ["args", "returns", "examples", "notes", "attributes", "yields", "raises"]
+SUMMARY_BADGE_MAP = {"Classes": "class", "Properties": "property", "Methods": "method", "Functions": "function"}
+
+
+def contribution_admonition(pretty: str, url: str, *, kind: str = "note", title: str | None = None) -> str:
+    """Return a standardized contribution call-to-action admonition."""
+    label = f' "{title}"' if title else ""
+    body = (
+        f"This page is sourced from [{pretty}]({url}). Have an improvement or example to add? "
+        f"Open a [Pull Request](https://docs.ultralytics.com/help/contributing/) — thank you! 🙏"
+    )
+    return f"!!! {kind}{label}\n\n    {body}\n\n"
+
+
+def _relative_to_workspace(path: Path) -> Path:
+    """Return path relative to workspace root when possible."""
+    try:
+        return path.relative_to(PACKAGE_DIR.parent)
+    except ValueError:
+        return path
+
+
+def render_source_panel(item: DocItem, module_url: str, module_path: str) -> str:
+    """Render a collapsible source panel with a GitHub link."""
+    if not item.source:
+        return ""
+    source_url = f"{module_url}#L{item.lineno}-L{item.end_lineno}"
+    summary = f"Source code in <code>{html.escape(module_path)}.py</code>"
+    return (
+        "<details>\n"
+        f"<summary>{summary}</summary>\n\n"
+        f'<a href="{source_url}">{GITHUB_ICON}View on GitHub</a>\n'
+        f"{_code_fence(item.source)}\n"
+        "</details>\n"
+    )
+
+
+def render_docstring(
+    doc: ParsedDocstring,
+    level: int,
+    signature_params: list[ParameterDoc] | None = None,
+    section_order: list[str] | None = None,
+    extra_sections: dict[str, str] | None = None,
+) -> str:
+    """Convert a ParsedDocstring into Markdown with tables similar to mkdocstrings."""
+    parts: list[str] = []
+    if doc.summary:
+        parts.append(doc.summary)
+    if doc.description:
+        parts.append(doc.description)
+
+    sig_params = signature_params or []
+    merged_params = _merge_params(doc.params, sig_params)
+
+    sections: dict[str, str] = {}
+
+    if merged_params:
+        rows = []
+        for p in merged_params:
+            default_val = f"`{p.default}`" if p.default not in (None, "") else "*required*"
+            rows.append(
+                [
+                    f"`{p.name}`",
+                    f"`{p.type}`" if p.type else "",
+                    p.description.strip() if p.description else "",
+                    default_val,
+                ]
+            )
+        table = _render_table(["Name", "Type", "Description", "Default"], rows, level, title=None)
+        sections["args"] = f"**Args**\n\n{table}"
+
+    if doc.returns:
+        rows = []
+        for r in doc.returns:
+            rows.append([f"`{r.type}`" if r.type else "", r.description])
+        table = _render_table(["Type", "Description"], rows, level, title=None)
+        sections["returns"] = f"**Returns**\n\n{table}"
+
+    if doc.examples:
+        code_block = "\n\n".join(f"```python\n{example.strip()}\n```" for example in doc.examples if example.strip())
+        if code_block:
+            sections["examples"] = f"**Examples**\n\n{code_block}\n\n"
+
+    if doc.notes:
+        note_text = "\n\n".join(doc.notes).strip()
+        indented = textwrap.indent(note_text, "    ")
+        sections["notes"] = f'!!! note "Notes"\n\n{indented}\n\n'
+
+    if doc.attributes:
+        rows = []
+        for a in doc.attributes:
+            rows.append(
+                [f"`{a.name}`", f"`{a.type}`" if a.type else "", a.description.strip() if a.description else ""]
+            )
+        table = _render_table(["Name", "Type", "Description"], rows, level, title=None)
+        sections["attributes"] = f"**Attributes**\n\n{table}"
+
+    if doc.yields:
+        rows = []
+        for r in doc.yields:
+            rows.append([f"`{r.type}`" if r.type else "", r.description])
+        table = _render_table(["Type", "Description"], rows, level, title=None)
+        sections["yields"] = f"**Yields**\n\n{table}"
+
+    if doc.raises:
+        rows = []
+        for e in doc.raises:
+            type_cell = e.type or e.name
+            rows.append([f"`{type_cell}`" if type_cell else "", e.description or ""])
+        table = _render_table(["Type", "Description"], rows, level, title=None)
+        sections["raises"] = f"**Raises**\n\n{table}"
+
+    if extra_sections:
+        sections.update({k: v for k, v in extra_sections.items() if v})
+    # Ensure section order contains unique entries to avoid duplicate renders (e.g., classes injecting "examples")
+    order = list(dict.fromkeys(section_order or DEFAULT_SECTION_ORDER))
+
+    ordered_sections: list[str] = []
+    seen = set()
+    for key in order:
+        section = sections.get(key)
+        if section:
+            ordered_sections.append(section)
+            seen.add(key)
+
+    for key, section in sections.items():
+        if key not in seen:
+            ordered_sections.append(section)
+
+    parts.extend(filter(None, ordered_sections))
+    return "\n\n".join([p.rstrip() for p in parts if p]).strip() + ("\n\n" if parts else "")
+
+
+def item_anchor(item: DocItem) -> str:
+    """Create a stable anchor for a documented item."""
+    return item.qualname
+
+
+def display_qualname(item: DocItem) -> str:
+    """Return a cleaned, fully-qualified name for display (strip __init__ noise)."""
+    return item.qualname.replace(".__init__.", ".")
+
+
+def render_summary_tabs(module: DocumentedModule) -> str:
+    """Render a tabbed summary of classes, methods, and functions for quick navigation."""
+    tab_entries: list[tuple[str, list[str]]] = []
+
+    if module.classes:
+        tab_entries.append(
+            (
+                "Classes",
+                [f"- [`{cls.name}`](#{item_anchor(cls)})" for cls in module.classes],
+            )
+        )
+
+    property_links = []
+    method_links = []
+    for cls in module.classes:
+        for child in cls.children:
+            if child.kind == "property":
+                property_links.append(f"- [`{cls.name}.{child.name}`](#{item_anchor(child)})")
+        for child in cls.children:
+            if child.kind == "method":
+                method_links.append(f"- [`{cls.name}.{child.name}`](#{item_anchor(child)})")
+    if property_links:
+        tab_entries.append(("Properties", property_links))
+    if method_links:
+        tab_entries.append(("Methods", method_links))
+
+    if module.functions:
+        tab_entries.append(
+            (
+                "Functions",
+                [f"- [`{func.name}`](#{item_anchor(func)})" for func in module.functions],
+            )
+        )
+
+    if not tab_entries:
+        return ""
+
+    lines = ['!!! abstract "Summary"\n']
+    for label, bullets in tab_entries:
+        badge_class = SUMMARY_BADGE_MAP.get(label, label.lower())
+        label_badge = f'<span class="doc-kind doc-kind-{badge_class}">{label}</span>'
+        lines.append(f'    === "{label_badge}"\n')
+        lines.append("\n".join(f"        {line}" for line in bullets))
+        lines.append("")  # Blank line after each tab block
+    return "\n".join(lines).rstrip() + "\n\n"
+
+
+def render_item(item: DocItem, module_url: str, module_path: str, level: int = 2) -> str:
+    """Render a class, function, or method to Markdown."""
+    anchor = item_anchor(item)
+    title_prefix = item.kind.capitalize()
+    anchor_id = anchor.replace("_", r"\_")  # escape underscores so attr_list keeps them in the id
+    heading = f"{'#' * level} {title_prefix} `{display_qualname(item)}` {{#{anchor_id}}}"
+    signature_block = f"```python\n{item.signature}\n```\n"
+
+    parts = [heading, signature_block]
+
+    if item.bases:
+        bases = ", ".join(f"`{b}`" for b in item.bases)
+        parts.append(f"**Bases:** {bases}\n")
+
+    if item.kind == "class":
+        method_section = None
+        if item.children:
+            props = [c for c in item.children if c.kind == "property"]
+            methods = [c for c in item.children if c.kind == "method"]
+            methods.sort(key=lambda m: (not m.name.startswith("__"), m.name))
+
+            rows = []
+            for child in props + methods:
+                summary = child.doc.summary or (
+                    _normalize_text(child.doc.description).split("\n\n")[0] if child.doc.description else ""
+                )
+                rows.append([f"[`{child.name}`](#{item_anchor(child)})", summary.strip()])
+            if rows:
+                table = _render_table(["Name", "Description"], rows, level + 1, title=None)
+                method_section = f"**Methods**\n\n{table}"
+
+        order = ["args", "attributes", "methods", "examples", *DEFAULT_SECTION_ORDER]
+        rendered = render_docstring(
+            item.doc,
+            level + 1,
+            signature_params=item.signature_params,
+            section_order=order,
+            extra_sections={"methods": method_section} if method_section else None,
+        )
+        parts.append(rendered)
+    else:
+        parts.append(render_docstring(item.doc, level + 1, signature_params=item.signature_params))
+
+    if item.kind == "class" and item.source:
+        parts.append(render_source_panel(item, module_url, module_path))
+
+    if item.children:
+        props = [c for c in item.children if c.kind == "property"]
+        methods = [c for c in item.children if c.kind == "method"]
+        methods.sort(key=lambda m: (not m.name.startswith("__"), m.name))
+
+        ordered_children = props + methods
+        parts.append("<br>\n")
+        for idx, child in enumerate(ordered_children):
+            parts.append(render_item(child, module_url, module_path, level + 1))
+            if idx != len(ordered_children) - 1:
+                parts.append("<br>\n")
+
+    if item.source and item.kind != "class":
+        parts.append(render_source_panel(item, module_url, module_path))
+
+    return "\n\n".join(p.rstrip() for p in parts if p).rstrip() + "\n\n"
+
+
+def render_module_markdown(module: DocumentedModule) -> str:
+    """Render the full module reference content."""
+    module_path = module.module_path.replace(".", "/")
+    module_url = f"https://github.com/{GITHUB_REPO}/blob/main/{module_path}.py"
+    content: list[str] = ["<br>\n"]
+
+    summary_tabs = render_summary_tabs(module)
+    if summary_tabs:
+        content.append(summary_tabs)
+
+    sections: list[str] = []
+    for idx, cls in enumerate(module.classes):
+        sections.append(render_item(cls, module_url, module_path, level=2))
+        if idx != len(module.classes) - 1 or module.functions:
+            sections.append("<br><br><hr><br>\n")
+    for idx, func in enumerate(module.functions):
+        sections.append(render_item(func, module_url, module_path, level=2))
+        if idx != len(module.functions) - 1:
+            sections.append("<br><br><hr><br>\n")
+
+    content.extend(sections)
+    return "\n".join(content).rstrip() + "\n\n<br><br>\n"
+
+
+def create_markdown(module: DocumentedModule) -> Path:
+    """Create a Markdown file containing the API reference for the given Python module."""
+    md_filepath = REFERENCE_DIR / module.path.relative_to(PACKAGE_DIR).with_suffix(".md")
+    exists = md_filepath.exists()
+
+    header_content = ""
+    if exists:
+        for part in md_filepath.read_text().split("---"):
+            if "description:" in part or "comments:" in part:
+                header_content += f"---{part}---\n\n"
+    if not header_content:
+        header_content = "---\ndescription: TODO ADD DESCRIPTION\nkeywords: TODO ADD KEYWORDS\n---\n\n"
+
+    module_path_fs = module.module_path.replace(".", "/")
+    url = f"https://github.com/{GITHUB_REPO}/blob/main/{module_path_fs}.py"
+    pretty = url.replace("__init__.py", "\\_\\_init\\_\\_.py")  # Properly display __init__.py filenames
+
+    title_content = f"# Reference for `{module_path_fs}.py`\n\n" + contribution_admonition(
+        pretty, url, kind="success", title="Improvements"
+    )
+
+    md_filepath.parent.mkdir(parents=True, exist_ok=True)
+    md_filepath.write_text(header_content + title_content + render_module_markdown(module))
+
+    if not exists:
+        subprocess.run(["git", "add", "-f", str(md_filepath)], check=True, cwd=REPO_ROOT)
+
+    return _relative_to_workspace(md_filepath)
+
+
+def nested_dict():
+    """Create and return a nested defaultdict."""
+    return defaultdict(nested_dict)
+
+
+def sort_nested_dict(d: dict) -> dict:
+    """Sort a nested dictionary recursively."""
+    return {k: sort_nested_dict(v) if isinstance(v, dict) else v for k, v in sorted(d.items())}
+
+
+def create_nav_menu_yaml(nav_items: list[str]) -> str:
+    """Create and return a YAML string for the navigation menu."""
+    nav_tree = nested_dict()
+
+    for item_str in nav_items:
+        item = Path(item_str)
+        parts = item.parts
+        current_level = nav_tree["reference"]
+        for part in parts[2:-1]:  # Skip docs/reference and filename
+            current_level = current_level[part]
+        current_level[parts[-1].replace(".md", "")] = item
+
+    def _dict_to_yaml(d, level=0):
+        """Convert a nested dictionary to a YAML-formatted string with indentation."""
+        yaml_str = ""
+        indent = "  " * level
+        for k, v in sorted(d.items()):
+            if isinstance(v, dict):
+                yaml_str += f"{indent}- {k}:\n{_dict_to_yaml(v, level + 1)}"
+            else:
+                yaml_str += f"{indent}- {k}: {str(v).replace('docs/en/', '')}\n"
+        return yaml_str
+
+    reference_yaml = _dict_to_yaml(sort_nested_dict(nav_tree))
+    print(f"Scan complete, generated reference section with {len(reference_yaml.splitlines())} lines")
+    return reference_yaml
+
+
+def extract_document_paths(yaml_section: str) -> list[str]:
+    """Extract document paths from a YAML section, ignoring formatting and structure."""
+    paths = []
+    # Match all paths that appear after a colon in the YAML
+    path_matches = re.findall(r":\s*([^\s][^:\n]*?)(?:\n|$)", yaml_section)
+    for path in path_matches:
+        # Clean up the path
+        path = path.strip()
+        if path and not path.startswith("-") and not path.endswith(":"):
+            paths.append(path)
+    return sorted(paths)
+
+
+def update_mkdocs_file(reference_yaml: str) -> None:
+    """Update the mkdocs.yaml file with the new reference section only if changes in document paths are detected."""
+    mkdocs_content = MKDOCS_YAML.read_text()
+
+    # Find the top-level Reference section
+    ref_pattern = r"(\n  - Reference:[\s\S]*?)(?=\n  - \w|$)"
+    ref_match = re.search(ref_pattern, mkdocs_content)
+
+    # Build new section with proper indentation
+    new_section_lines = ["\n  - Reference:"]
+    new_section_lines.extend(
+        f"    {line}"
+        for line in reference_yaml.splitlines()
+        if line.strip() != "- reference:"  # Skip redundant header
+    )
+    new_ref_section = "\n".join(new_section_lines) + "\n"
+
+    if ref_match:
+        # We found an existing Reference section
+        ref_section = ref_match.group(1)
+        print(f"Found existing top-level Reference section ({len(ref_section)} chars)")
+
+        # Compare only document paths
+        existing_paths = extract_document_paths(ref_section)
+        new_paths = extract_document_paths(new_ref_section)
+
+        # Check if the document paths are the same (ignoring structure or formatting differences)
+        if len(existing_paths) == len(new_paths) and set(existing_paths) == set(new_paths):
+            print(f"No changes detected in document paths ({len(existing_paths)} items). Skipping update.")
+            return
+
+        print(f"Changes detected: {len(new_paths)} document paths vs {len(existing_paths)} existing")
+
+        # Update content
+        new_content = mkdocs_content.replace(ref_section, new_ref_section)
+        MKDOCS_YAML.write_text(new_content)
+        subprocess.run(["npx", "prettier", "--write", str(MKDOCS_YAML)], check=False, cwd=PACKAGE_DIR.parent)
+        print(f"Updated Reference section in {MKDOCS_YAML}")
+    elif help_match := re.search(r"(\n  - Help:)", mkdocs_content):
+        # No existing Reference section, we need to add it
+        help_section = help_match.group(1)
+        # Insert before Help section
+        new_content = mkdocs_content.replace(help_section, f"{new_ref_section}{help_section}")
+        MKDOCS_YAML.write_text(new_content)
+        print(f"Added new Reference section before Help in {MKDOCS_YAML}")
+    else:
+        print("Could not find a suitable location to add Reference section")
+
+
+def _finalize_reference(nav_items: list[str], update_nav: bool, created: int, created_label: str) -> list[str]:
+    """Optionally sync navigation and print creation summary."""
+    if update_nav:
+        update_mkdocs_file(create_nav_menu_yaml(nav_items))
+    if created:
+        print(f"Created {created} new {created_label}")
+    return nav_items
+
+
+def build_reference(update_nav: bool = True) -> list[str]:
+    """Create placeholder reference files (legacy mkdocstrings flow)."""
+    return build_reference_placeholders(update_nav=update_nav)
+
+
+def build_reference_placeholders(update_nav: bool = True) -> list[str]:
+    """Create minimal placeholder reference files (mkdocstrings-style) and optionally update nav."""
+    nav_items: list[str] = []
+    created = 0
+
+    for py_filepath in TQDM(list(PACKAGE_DIR.rglob("*.py")), desc="Building reference stubs", unit="file"):
+        classes, functions = extract_classes_and_functions(py_filepath)
+        if not classes and not functions:
+            continue
+        module_path = (
+            f"{PACKAGE_DIR.name}.{py_filepath.relative_to(PACKAGE_DIR).with_suffix('').as_posix().replace('/', '.')}"
+        )
+        exists = (REFERENCE_DIR / py_filepath.relative_to(PACKAGE_DIR).with_suffix(".md")).exists()
+        md_rel = create_placeholder_markdown(py_filepath, module_path, classes, functions)
+        nav_items.append(str(md_rel))
+        if not exists:
+            created += 1
+    if update_nav:
+        update_mkdocs_file(create_nav_menu_yaml(nav_items))
+    if created:
+        print(f"Created {created} new reference stub files")
+    return nav_items
+
+
+def build_reference_docs(update_nav: bool = False) -> list[str]:
+    """Render full docstring-based reference content."""
+    nav_items: list[str] = []
+    created = 0
+
+    desc = f"Docstrings {GITHUB_REPO or PACKAGE_DIR.name}"
+    for py_filepath in TQDM(list(PACKAGE_DIR.rglob("*.py")), desc=desc, unit="file"):
+        md_target = REFERENCE_DIR / py_filepath.relative_to(PACKAGE_DIR).with_suffix(".md")
+        exists_before = md_target.exists()
+        module = parse_module(py_filepath)
+        if not module or (not module.classes and not module.functions):
+            continue
+        md_rel_filepath = create_markdown(module)
+        if not exists_before:
+            created += 1
+        nav_items.append(str(md_rel_filepath))
+
+    if update_nav:
+        update_mkdocs_file(create_nav_menu_yaml(nav_items))
+    if created:
+        print(f"Created {created} new reference files")
+    return nav_items
+
+
+def build_reference_for(
+    package_dir: Path, reference_dir: Path, github_repo: str, update_nav: bool = False
+) -> list[str]:
+    """Temporarily switch package context to build reference docs for another project."""
+    global PACKAGE_DIR, REFERENCE_DIR, GITHUB_REPO
+    prev = (PACKAGE_DIR, REFERENCE_DIR, GITHUB_REPO)
+    try:
+        PACKAGE_DIR, REFERENCE_DIR, GITHUB_REPO = package_dir, reference_dir, github_repo
+        return build_reference_docs(update_nav=update_nav)
+    finally:
+        PACKAGE_DIR, REFERENCE_DIR, GITHUB_REPO = prev
+
+
+def main():
+    """CLI entrypoint."""
+    build_reference(update_nav=True)
+
+
+if __name__ == "__main__":
+    main()

ultralytics-main/docs/coming_soon_template.md

@@ -0,0 +1,34 @@
+---
+description: Discover what's next for Ultralytics with our under-construction page, previewing new, groundbreaking AI and ML features coming soon.
+keywords: Ultralytics, coming soon, under construction, new features, AI updates, ML advancements, YOLO, technology preview
+---
+
+# Under Construction 🏗️🌟
+
+Welcome to the [Ultralytics](https://www.ultralytics.com/) "Under Construction" page! Here, we're hard at work developing the [next generation](https://www.ultralytics.com/glossary/foundation-model) of [AI](https://www.ultralytics.com/glossary/artificial-intelligence-ai) and [ML](https://www.ultralytics.com/glossary/machine-learning-ml) innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you!
+
+## Exciting New Features on the Way 🎉
+
+- **Innovative Breakthroughs:** Get ready for [advanced features](https://docs.ultralytics.com/) and services designed to [transform your AI and ML experience](https://www.ultralytics.com/solutions).
+- **New Horizons:** Anticipate novel products that [redefine AI and ML capabilities](https://docs.ultralytics.com/tasks/).
+- **Enhanced Services:** We're upgrading our [services](https://www.ultralytics.com/hub) for greater [efficiency](https://docs.ultralytics.com/modes/benchmark/) and user-friendliness.
+
+## Stay Updated 🚧
+
+This page is your go-to resource for the latest integration updates and feature rollouts. Stay connected through:
+
+- **Newsletter:** Subscribe to [our Ultralytics newsletter](https://www.ultralytics.com/#newsletter) for announcements, releases, and early access updates.
+- **Social Media:** Follow [Ultralytics on LinkedIn](https://www.linkedin.com/company/ultralytics) for behind-the-scenes content, product news, and community highlights.
+- **Blog:** Dive into the [Ultralytics AI blog](https://www.ultralytics.com/blog) for in-depth articles, tutorials, and use-case spotlights.
+
+## We Value Your Input 🗣️
+
+Help shape the future of Ultralytics HUB by sharing your ideas, feedback, and integration requests through our [official contact form](https://www.ultralytics.com/contact).
+
+## Thank You, Community! 🌍
+
+Your [contributions](https://docs.ultralytics.com/help/contributing/) and ongoing support fuel our commitment to pushing the boundaries of [AI innovation](https://github.com/ultralytics/ultralytics). Stay tuned—exciting things are just around the corner!
+
+---
+
+Excited for what's coming? Bookmark this page and check out our [Quickstart Guide](https://docs.ultralytics.com/quickstart/) to get started with our current tools while you wait. Get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖

ultralytics-main/docs/en/CNAME

@@ -0,0 +1 @@
+docs.ultralytics.com

ultralytics-main/docs/en/datasets/classify/caltech101.md

@@ -0,0 +1,167 @@
+---
+comments: true
+description: Explore the widely-used Caltech-101 dataset with 9,000 images across 101 categories. Ideal for object recognition tasks in machine learning and computer vision.
+keywords: Caltech-101, dataset, object recognition, machine learning, computer vision, YOLO, deep learning, research, AI
+---
+
+# Caltech-101 Dataset
+
+The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is a widely used dataset for object recognition tasks, containing around 9,000 images from 101 object categories. The categories were chosen to reflect a variety of real-world objects, and the images themselves were carefully selected and annotated to provide a challenging benchmark for object recognition algorithms.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/isc06_9qnM0"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics HUB
+</p>
+
+!!! note "Automatic Data Splitting"
+
+    The Caltech-101 dataset, as provided, does not come with pre-defined train/validation splits. However, when you use the training commands provided in the usage examples below, the Ultralytics framework will automatically split the dataset for you. The default split used is 80% for the training set and 20% for the validation set.
+
+## Key Features
+
+- The Caltech-101 dataset comprises around 9,000 color images divided into 101 categories.
+- The categories encompass a wide variety of objects, including animals, vehicles, household items, and people.
+- The number of images per category varies, with about 40 to 800 images in each category.
+- Images are of variable sizes, with most images being medium resolution.
+- Caltech-101 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.
+
+## Dataset Structure
+
+Unlike many other datasets, the Caltech-101 dataset is not formally split into training and testing sets. Users typically create their own splits based on their specific needs. However, a common practice is to use a random subset of images for training (e.g., 30 images per category) and the remaining images for testing.
+
+## Applications
+
+The Caltech-101 dataset is extensively used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object recognition tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. Its wide variety of categories and high-quality images make it an excellent dataset for research and development in the field of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
+
+## Usage
+
+To train a YOLO model on the Caltech-101 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="caltech101", epochs=100, imgsz=416)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
+        ```
+
+## Sample Images and Annotations
+
+The Caltech-101 dataset contains high-quality color images of various objects, providing a well-structured dataset for [image classification](https://www.ultralytics.com/glossary/image-classification) tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/caltech101-sample-image.avif)
+
+The example showcases the variety and complexity of the objects in the Caltech-101 dataset, emphasizing the significance of a diverse dataset for training robust object recognition models.
+
+## Citations and Acknowledgments
+
+If you use the Caltech-101 dataset in your research or development work, please cite the following paper:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{fei2007learning,
+          title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
+          author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
+          journal={Computer vision and Image understanding},
+          volume={106},
+          number={1},
+          pages={59--70},
+          year={2007},
+          publisher={Elsevier}
+        }
+        ```
+
+We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).
+
+## FAQ
+
+### What is the Caltech-101 dataset used for in machine learning?
+
+The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is widely used in machine learning for object recognition tasks. It contains around 9,000 images across 101 categories, providing a challenging benchmark for evaluating object recognition algorithms. Researchers leverage it to train and test models, especially Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs) and Support Vector Machines (SVMs), in computer vision.
+
+### How can I train an Ultralytics YOLO model on the Caltech-101 dataset?
+
+To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the provided code snippets. For example, to train for 100 epochs:
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="caltech101", epochs=100, imgsz=416)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=caltech101 model=yolo11n-cls.pt epochs=100 imgsz=416
+        ```
+
+For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.
+
+### What are the key features of the Caltech-101 dataset?
+
+The Caltech-101 dataset includes:
+
+- Around 9,000 color images across 101 categories.
+- Categories covering a diverse range of objects, including animals, vehicles, and household items.
+- Variable number of images per category, typically between 40 and 800.
+- Variable image sizes, with most being medium resolution.
+
+These features make it an excellent choice for training and evaluating object recognition models in machine learning and computer vision.
+
+### Why should I cite the Caltech-101 dataset in my research?
+
+Citing the Caltech-101 dataset in your research acknowledges the creators' contributions and provides a reference for others who might use the dataset. The recommended citation is:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{fei2007learning,
+          title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
+          author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
+          journal={Computer vision and Image understanding},
+          volume={106},
+          number={1},
+          pages={59--70},
+          year={2007},
+          publisher={Elsevier}
+        }
+        ```
+
+Citing helps in maintaining the integrity of academic work and assists peers in locating the original resource.
+
+### Can I use Ultralytics HUB for training models on the Caltech-101 dataset?
+
+Yes, you can use [Ultralytics HUB](https://www.ultralytics.com/hub) for training models on the Caltech-101 dataset. Ultralytics HUB provides an intuitive platform for managing datasets, training models, and deploying them without extensive coding. For a detailed guide, refer to the [how to train your custom models with Ultralytics HUB](https://www.ultralytics.com/blog/how-to-train-your-custom-models-with-ultralytics-hub) blog post.

ultralytics-main/docs/en/datasets/classify/caltech256.md

@@ -0,0 +1,148 @@
+---
+comments: true
+description: Explore the Caltech-256 dataset, featuring 30,000 images across 257 categories, ideal for training and testing object recognition algorithms.
+keywords: Caltech-256 dataset, object classification, image dataset, machine learning, computer vision, deep learning, YOLO, training dataset
+---
+
+# Caltech-256 Dataset
+
+The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is an extensive collection of images used for object classification tasks. It contains around 30,000 images divided into 257 categories (256 object categories and 1 background category). The images are carefully curated and annotated to provide a challenging and diverse benchmark for object recognition algorithms.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/isc06_9qnM0"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> How to Train <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model using Caltech-256 Dataset with Ultralytics HUB
+</p>
+
+!!! note "Automatic Data Splitting"
+
+    The Caltech-256 dataset, as provided, does not come with pre-defined train/validation splits. However, when you use the training commands provided in the usage examples below, the Ultralytics framework will automatically split the dataset for you. The default split used is 80% for the training set and 20% for the validation set.
+
+## Key Features
+
+- The Caltech-256 dataset comprises around 30,000 color images divided into 257 categories.
+- Each category contains a minimum of 80 images.
+- The categories encompass a wide variety of real-world objects, including animals, vehicles, household items, and people.
+- Images are of variable sizes and resolutions.
+- Caltech-256 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.
+
+## Dataset Structure
+
+Like [Caltech-101](../classify/caltech101.md), the Caltech-256 dataset does not have a formal split between training and testing sets. Users typically create their own splits according to their specific needs. A common practice is to use a random subset of images for training and the remaining images for testing.
+
+## Applications
+
+The Caltech-256 dataset is extensively used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in object recognition tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. Its diverse set of categories and high-quality images make it an invaluable dataset for research and development in the field of machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
+
+## Usage
+
+To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="caltech256", epochs=100, imgsz=416)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
+        ```
+
+## Sample Images and Annotations
+
+The Caltech-256 dataset contains high-quality color images of various objects, providing a comprehensive dataset for object recognition tasks. Here are some examples of images from the dataset ([credit](https://ml4a.github.io/demos/tsne_viewer.html)):
+
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/caltech256-sample-image.avif)
+
+The example showcases the diversity and complexity of the objects in the Caltech-256 dataset, emphasizing the importance of a varied dataset for training robust object recognition models.
+
+## Citations and Acknowledgments
+
+If you use the Caltech-256 dataset in your research or development work, please cite the following paper:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{griffin2007caltech,
+                 title={Caltech-256 object category dataset},
+                 author={Griffin, Gregory and Holub, Alex and Perona, Pietro},
+                 year={2007}
+        }
+        ```
+
+We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
+
+## FAQ
+
+### What is the Caltech-256 dataset and why is it important for machine learning?
+
+The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is a large image dataset used primarily for object classification tasks in machine learning and computer vision. It consists of around 30,000 color images divided into 257 categories, covering a wide range of real-world objects. The dataset's diverse and high-quality images make it an excellent benchmark for evaluating object recognition algorithms, which is crucial for developing robust machine learning models.
+
+### How can I train a YOLO model on the Caltech-256 dataset using Python or CLI?
+
+To train a YOLO model on the Caltech-256 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following code snippets. Refer to the model [Training](../../modes/train.md) page for additional options.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model
+
+        # Train the model
+        results = model.train(data="caltech256", epochs=100, imgsz=416)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=caltech256 model=yolo11n-cls.pt epochs=100 imgsz=416
+        ```
+
+### What are the most common use cases for the Caltech-256 dataset?
+
+The Caltech-256 dataset is widely used for various object recognition tasks such as:
+
+- Training Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs)
+- Evaluating the performance of Support Vector Machines (SVMs)
+- Benchmarking new deep learning algorithms
+- Developing [object detection](https://www.ultralytics.com/glossary/object-detection) models using frameworks like Ultralytics YOLO
+
+Its diversity and comprehensive annotations make it ideal for research and development in machine learning and computer vision.
+
+### How is the Caltech-256 dataset structured and split for training and testing?
+
+The Caltech-256 dataset does not come with a predefined split for training and testing. Users typically create their own splits according to their specific needs. A common approach is to randomly select a subset of images for training and use the remaining images for testing. This flexibility allows users to tailor the dataset to their specific project requirements and experimental setups.
+
+### Why should I use Ultralytics YOLO for training models on the Caltech-256 dataset?
+
+Ultralytics YOLO models offer several advantages for training on the Caltech-256 dataset:
+
+- **High Accuracy**: YOLO models are known for their state-of-the-art performance in object detection tasks.
+- **Speed**: They provide real-time inference capabilities, making them suitable for applications requiring quick predictions.
+- **Ease of Use**: With [Ultralytics HUB](https://www.ultralytics.com/hub), users can train, validate, and deploy models without extensive coding.
+- **Pretrained Models**: Starting from pretrained models, like `yolo11n-cls.pt`, can significantly reduce training time and improve model [accuracy](https://www.ultralytics.com/glossary/accuracy).
+
+For more details, explore our [comprehensive training guide](../../modes/train.md) and learn about [image classification](../../tasks/classify.md) with Ultralytics YOLO.

ultralytics-main/docs/en/datasets/classify/cifar10.md

@@ -0,0 +1,173 @@
+---
+comments: true
+description: Explore the CIFAR-10 dataset, featuring 60,000 color images in 10 classes. Learn about its structure, applications, and how to train models using YOLO.
+keywords: CIFAR-10, dataset, machine learning, computer vision, image classification, YOLO, deep learning, neural networks
+---
+
+# CIFAR-10 Dataset
+
+The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a collection of images used widely for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision algorithms. It was developed by researchers at the CIFAR institute and consists of 60,000 32x32 color images in 10 different classes.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/fLBbyhPbWzY"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> How to Train an <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> Model with CIFAR-10 Dataset using Ultralytics YOLO11
+</p>
+
+## Key Features
+
+- The CIFAR-10 dataset consists of 60,000 images, divided into 10 classes.
+- Each class contains 6,000 images, split into 5,000 for training and 1,000 for testing.
+- The images are colored and of size 32x32 pixels.
+- The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
+- CIFAR-10 is commonly used for training and testing in the field of machine learning and computer vision.
+
+## Dataset Structure
+
+The CIFAR-10 dataset is split into two subsets:
+
+1. **Training Set**: This subset contains 50,000 images used for training machine learning models.
+2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
+
+## Applications
+
+The CIFAR-10 dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a well-rounded dataset for research and development in the field of machine learning and computer vision.
+
+## Usage
+
+To train a YOLO model on the CIFAR-10 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="cifar10", epochs=100, imgsz=32)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
+        ```
+
+## Sample Images and Annotations
+
+The CIFAR-10 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/cifar10-sample-image.avif)
+
+The example showcases the variety and complexity of the objects in the CIFAR-10 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## Citations and Acknowledgments
+
+If you use the CIFAR-10 dataset in your research or development work, please cite the following paper:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @TECHREPORT{Krizhevsky09learningmultiple,
+                    author={Alex Krizhevsky},
+                    title={Learning multiple layers of features from tiny images},
+                    institution={},
+                    year={2009}
+        }
+        ```
+
+We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
+
+## FAQ
+
+### How can I train a YOLO model on the CIFAR-10 dataset?
+
+To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow the examples provided for both Python and CLI. Here is a basic example to train your model for 100 epochs with an image size of 32x32 pixels:
+
+!!! example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="cifar10", epochs=100, imgsz=32)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=cifar10 model=yolo11n-cls.pt epochs=100 imgsz=32
+        ```
+
+For more details, refer to the model [Training](../../modes/train.md) page.
+
+### What are the key features of the CIFAR-10 dataset?
+
+The CIFAR-10 dataset consists of 60,000 color images divided into 10 classes. Each class contains 6,000 images, with 5,000 for training and 1,000 for testing. The images are 32x32 pixels in size and vary across the following categories:
+
+- Airplanes
+- Cars
+- Birds
+- Cats
+- Deer
+- Dogs
+- Frogs
+- Horses
+- Ships
+- Trucks
+
+This diverse dataset is essential for training image classification models in fields such as machine learning and computer vision. For more information, visit the CIFAR-10 sections on [dataset structure](#dataset-structure) and [applications](#applications).
+
+### Why use the CIFAR-10 dataset for image classification tasks?
+
+The CIFAR-10 dataset is an excellent benchmark for image classification due to its diversity and structure. It contains a balanced mix of 60,000 labeled images across 10 different categories, which helps in training robust and generalized models. It is widely used for evaluating deep learning models, including Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs) and other machine learning algorithms. The dataset is relatively small, making it suitable for quick experimentation and algorithm development. Explore its numerous applications in the [applications](#applications) section.
+
+### How is the CIFAR-10 dataset structured?
+
+The CIFAR-10 dataset is structured into two main subsets:
+
+1. **Training Set**: Contains 50,000 images used for training machine learning models.
+2. **Testing Set**: Consists of 10,000 images for testing and benchmarking the trained models.
+
+Each subset comprises images categorized into 10 classes, with their annotations readily available for model training and evaluation. For more detailed information, refer to the [dataset structure](#dataset-structure) section.
+
+### How can I cite the CIFAR-10 dataset in my research?
+
+If you use the CIFAR-10 dataset in your research or development projects, make sure to cite the following paper:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @TECHREPORT{Krizhevsky09learningmultiple,
+                    author={Alex Krizhevsky},
+                    title={Learning multiple layers of features from tiny images},
+                    institution={},
+                    year={2009}
+        }
+        ```
+
+Acknowledging the dataset's creators helps support continued research and development in the field. For more details, see the [citations and acknowledgments](#citations-and-acknowledgments) section.
+
+### What are some practical examples of using the CIFAR-10 dataset?
+
+The CIFAR-10 dataset is often used for training image classification models, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). These models can be employed in various computer vision tasks including [object detection](https://www.ultralytics.com/glossary/object-detection), [image recognition](https://www.ultralytics.com/glossary/image-recognition), and automated tagging. To see some practical examples, check the code snippets in the [usage](#usage) section.

ultralytics-main/docs/en/datasets/classify/cifar100.md

@@ -0,0 +1,141 @@
+---
+comments: true
+description: Explore the CIFAR-100 dataset, consisting of 60,000 32x32 color images across 100 classes. Ideal for machine learning and computer vision tasks.
+keywords: CIFAR-100, dataset, machine learning, computer vision, image classification, deep learning, YOLO, training, testing, Alex Krizhevsky
+---
+
+# CIFAR-100 Dataset
+
+The [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a significant extension of the CIFAR-10 dataset, composed of 60,000 32x32 color images in 100 different classes. It was developed by researchers at the CIFAR institute, offering a more challenging dataset for more complex machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/6bZeCs0xwO4"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> How to Train Ultralytics YOLO11 on CIFAR-100 | Step-by-Step Image Classification Tutorial 🚀
+</p>
+
+## Key Features
+
+- The CIFAR-100 dataset consists of 60,000 images, divided into 100 classes.
+- Each class contains 600 images, split into 500 for training and 100 for testing.
+- The images are colored and of size 32x32 pixels.
+- The 100 different classes are grouped into 20 coarse categories for higher level classification.
+- CIFAR-100 is commonly used for training and testing in the field of machine learning and computer vision.
+
+## Dataset Structure
+
+The CIFAR-100 dataset is split into two subsets:
+
+1. **Training Set**: This subset contains 50,000 images used for training machine learning models.
+2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
+
+## Applications
+
+The CIFAR-100 dataset is extensively used for training and evaluating deep learning models in [image classification](https://www.ultralytics.com/glossary/image-classification) tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a more challenging and comprehensive dataset for research and development in the field of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision.
+
+## Usage
+
+To train a YOLO model on the CIFAR-100 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="cifar100", epochs=100, imgsz=32)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
+        ```
+
+## Sample Images and Annotations
+
+The CIFAR-100 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/cifar100-sample-image.avif)
+
+The example showcases the variety and complexity of the objects in the CIFAR-100 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## Citations and Acknowledgments
+
+If you use the CIFAR-100 dataset in your research or development work, please cite the following paper:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @TECHREPORT{Krizhevsky09learningmultiple,
+                    author={Alex Krizhevsky},
+                    title={Learning multiple layers of features from tiny images},
+                    institution={},
+                    year={2009}
+        }
+        ```
+
+We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
+
+## FAQ
+
+### What is the CIFAR-100 dataset and why is it significant?
+
+The [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) is a large collection of 60,000 32x32 color images classified into 100 classes. Developed by the Canadian Institute For Advanced Research (CIFAR), it provides a challenging dataset ideal for complex machine learning and computer vision tasks. Its significance lies in the diversity of classes and the small size of the images, making it a valuable resource for training and testing [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models, like Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs), using frameworks such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/).
+
+### How do I train a YOLO model on the CIFAR-100 dataset?
+
+You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI commands. Here's how:
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="cifar100", epochs=100, imgsz=32)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=cifar100 model=yolo11n-cls.pt epochs=100 imgsz=32
+        ```
+
+For a comprehensive list of available arguments, please refer to the model [Training](../../modes/train.md) page.
+
+### What are the primary applications of the CIFAR-100 dataset?
+
+The CIFAR-100 dataset is extensively used in training and evaluating deep learning models for image classification. Its diverse set of 100 classes, grouped into 20 coarse categories, provides a challenging environment for testing algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning approaches. This dataset is a key resource in research and development within machine learning and computer vision fields, particularly for [object recognition](https://docs.ultralytics.com/tasks/classify/) and classification tasks.
+
+### How is the CIFAR-100 dataset structured?
+
+The CIFAR-100 dataset is split into two main subsets:
+
+1. **Training Set**: Contains 50,000 images used for training machine learning models.
+2. **Testing Set**: Consists of 10,000 images used for testing and benchmarking the trained models.
+
+Each of the 100 classes contains 600 images, with 500 images for training and 100 for testing, making it uniquely suited for rigorous academic and industrial research.
+
+### Where can I find sample images and annotations from the CIFAR-100 dataset?
+
+The CIFAR-100 dataset includes a variety of color images of various objects, making it a structured dataset for image classification tasks. You can refer to the documentation page to see [sample images and annotations](#sample-images-and-annotations). These examples highlight the dataset's diversity and complexity, important for training robust image classification models. For more datasets suitable for classification tasks, check out [Ultralytics' classification datasets overview](https://docs.ultralytics.com/datasets/classify/).

ultralytics-main/docs/en/datasets/classify/fashion-mnist.md

@@ -0,0 +1,141 @@
+---
+comments: true
+description: Explore the Fashion-MNIST dataset, a modern replacement for MNIST with 70,000 Zalando article images. Ideal for benchmarking machine learning models.
+keywords: Fashion-MNIST, image classification, Zalando dataset, machine learning, deep learning, CNN, dataset overview
+---
+
+# Fashion-MNIST Dataset
+
+The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is a database of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) algorithms.
+
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/eX5ad6udQ9Q"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> How to do <a href="https://www.ultralytics.com/glossary/image-classification">Image Classification</a> on Fashion MNIST Dataset using Ultralytics YOLO11
+</p>
+
+## Key Features
+
+- Fashion-MNIST contains 60,000 training images and 10,000 testing images of Zalando's article images.
+- The dataset comprises grayscale images of size 28x28 pixels.
+- Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.
+- Fashion-MNIST is widely used for training and testing in the field of machine learning, especially for image classification tasks.
+
+## Dataset Structure
+
+The Fashion-MNIST dataset is split into two subsets:
+
+1. **Training Set**: This subset contains 60,000 images used for training machine learning models.
+2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
+
+## Labels
+
+Each training and test example is assigned to one of the following labels:
+
+```
+0. T-shirt/top
+1. Trouser
+2. Pullover
+3. Dress
+4. Coat
+5. Sandal
+6. Shirt
+7. Sneaker
+8. Bag
+9. Ankle boot
+```
+
+## Applications
+
+The Fashion-MNIST dataset is widely used for training and evaluating deep learning models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), [Support Vector Machines](https://www.ultralytics.com/glossary/support-vector-machine-svm) (SVMs), and various other machine learning algorithms. The dataset's simple and well-structured format makes it an essential resource for researchers and practitioners in the field of machine learning and computer vision.
+
+## Usage
+
+To train a CNN model on the Fashion-MNIST dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 28x28, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
+        ```
+
+## Sample Images and Annotations
+
+The Fashion-MNIST dataset contains grayscale images of Zalando's article images, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/fashion-mnist-sample.avif)
+
+The example showcases the variety and complexity of the images in the Fashion-MNIST dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## Acknowledgments
+
+If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.
+
+## FAQ
+
+### What is the Fashion-MNIST dataset and how is it different from MNIST?
+
+The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is a collection of 70,000 grayscale images of Zalando's article images, intended as a modern replacement for the original MNIST dataset. It serves as a benchmark for machine learning models in the context of image classification tasks. Unlike MNIST, which contains handwritten digits, Fashion-MNIST consists of 28x28-pixel images categorized into 10 fashion-related classes, such as T-shirt/top, trouser, and ankle boot.
+
+### How can I train a YOLO model on the Fashion-MNIST dataset?
+
+To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use both Python and CLI commands. Here's a quick example to get you started:
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained model
+        model = YOLO("yolo11n-cls.pt")
+
+        # Train the model on Fashion-MNIST
+        results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
+        ```
+
+
+    === "CLI"
+
+        ```bash
+        yolo classify train data=fashion-mnist model=yolo11n-cls.pt epochs=100 imgsz=28
+        ```
+
+For more detailed training parameters, refer to the [Training page](../../modes/train.md).
+
+### Why should I use the Fashion-MNIST dataset for benchmarking my machine learning models?
+
+The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is widely recognized in the [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) community as a robust alternative to MNIST. It offers a more complex and varied set of images, making it an excellent choice for benchmarking image classification models. The dataset's structure, comprising 60,000 training images and 10,000 testing images, each labeled with one of 10 classes, makes it ideal for evaluating the performance of different machine learning algorithms in a more challenging context.
+
+### Can I use Ultralytics YOLO for image classification tasks like Fashion-MNIST?
+
+Yes, Ultralytics YOLO models can be used for image classification tasks, including those involving the Fashion-MNIST dataset. YOLO11, for example, supports various vision tasks such as detection, segmentation, and classification. To get started with image classification tasks, refer to the [Classification page](https://docs.ultralytics.com/tasks/classify/).
+
+### What are the key features and structure of the Fashion-MNIST dataset?
+
+The Fashion-MNIST dataset is divided into two main subsets: 60,000 training images and 10,000 testing images. Each image is a 28x28-pixel grayscale picture representing one of 10 fashion-related classes. The simplicity and well-structured format make it ideal for training and evaluating models in machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks. For more details on the dataset structure, see the [Dataset Structure section](#dataset-structure).
+
+### How can I acknowledge the use of the Fashion-MNIST dataset in my research?
+
+If you utilize the Fashion-MNIST dataset in your research or development projects, it's important to acknowledge it by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This helps in attributing the data to Zalando Research, who made the dataset available for public use.

ultralytics-main/docs/en/datasets/classify/imagenet.md

@@ -0,0 +1,132 @@
+---
+comments: true
+description: Explore the extensive ImageNet dataset and discover its role in advancing deep learning in computer vision. Access pretrained models and training examples.
+keywords: ImageNet, deep learning, visual recognition, computer vision, pretrained models, YOLO, dataset, object detection, image classification
+---
+
+# ImageNet Dataset
+
+[ImageNet](https://www.image-net.org/) is a large-scale database of annotated images designed for use in visual object recognition research. It contains over 14 million images, with each image annotated using WordNet synsets, making it one of the most extensive resources available for training [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
+
+## ImageNet Pretrained Models
+
+{% include "macros/yolo-cls-perf.md" %}
+
+## Key Features
+
+- ImageNet contains over 14 million high-resolution images spanning thousands of object categories.
+- The dataset is organized according to the WordNet hierarchy, with each synset representing a category.
+- ImageNet is widely used for training and benchmarking in the field of computer vision, particularly for [image classification](https://www.ultralytics.com/glossary/image-classification) and [object detection](https://www.ultralytics.com/glossary/object-detection) tasks.
+- The annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been instrumental in advancing computer vision research.
+
+## Dataset Structure
+
+The ImageNet dataset is organized using the WordNet hierarchy. Each node in the hierarchy represents a category, and each category is described by a synset (a collection of synonymous terms). The images in ImageNet are annotated with one or more synsets, providing a rich resource for training models to recognize various objects and their relationships.
+
+## ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
+
+The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/) has been an important event in the field of computer vision. It has provided a platform for researchers and developers to evaluate their algorithms and models on a large-scale dataset with standardized evaluation metrics. The ILSVRC has led to significant advancements in the development of deep learning models for image classification, object detection, and other computer vision tasks.
+
+## Applications
+
+The ImageNet dataset is widely used for training and evaluating deep learning models in various computer vision tasks, such as image classification, object detection, and object localization. Some popular deep learning architectures, such as [AlexNet](https://en.wikipedia.org/wiki/AlexNet), [VGG](https://arxiv.org/abs/1409.1556), and [ResNet](https://arxiv.org/abs/1512.03385), were developed and benchmarked using the ImageNet dataset.
+
+## Usage
+
+To train a deep learning model on the ImageNet dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="imagenet", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
+        ```
+
+## Sample Images and Annotations
+
+The ImageNet dataset contains high-resolution images spanning thousands of object categories, providing a diverse and extensive dataset for training and evaluating computer vision models. Here are some examples of images from the dataset:
+
+![Dataset sample images](https://github.com/ultralytics/docs/releases/download/0/imagenet-sample-images.avif)
+
+The example showcases the variety and complexity of the images in the ImageNet dataset, highlighting the importance of a diverse dataset for training robust computer vision models.
+
+## Citations and Acknowledgments
+
+If you use the ImageNet dataset in your research or development work, please cite the following paper:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{ILSVRC15,
+                 author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
+                 title={ImageNet Large Scale Visual Recognition Challenge},
+                 year={2015},
+                 journal={International Journal of Computer Vision (IJCV)},
+                 volume={115},
+                 number={3},
+                 pages={211-252}
+        }
+        ```
+
+We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
+
+## FAQ
+
+### What is the ImageNet dataset and how is it used in computer vision?
+
+The [ImageNet dataset](https://www.image-net.org/) is a large-scale database consisting of over 14 million high-resolution images categorized using WordNet synsets. It is extensively used in visual object recognition research, including image classification and object detection. The dataset's annotations and sheer volume provide a rich resource for training deep learning models. Notably, models like AlexNet, VGG, and ResNet have been trained and benchmarked using ImageNet, showcasing its role in advancing computer vision.
+
+### How can I use a pretrained YOLO model for image classification on the ImageNet dataset?
+
+To use a pretrained Ultralytics YOLO model for image classification on the ImageNet dataset, follow these steps:
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="imagenet", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=imagenet model=yolo11n-cls.pt epochs=100 imgsz=224
+        ```
+
+For more in-depth training instruction, refer to our [Training page](../../modes/train.md).
+
+### Why should I use the Ultralytics YOLO11 pretrained models for my ImageNet dataset projects?
+
+Ultralytics YOLO11 pretrained models offer state-of-the-art performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for various computer vision tasks. For example, the YOLO11n-cls model, with a top-1 accuracy of 70.0% and a top-5 accuracy of 89.4%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLO11 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
+
+### How is the ImageNet dataset structured, and why is it important?
+
+The ImageNet dataset is organized using the WordNet hierarchy, where each node in the hierarchy represents a category described by a synset (a collection of synonymous terms). This structure allows for detailed annotations, making it ideal for training models to recognize a wide variety of objects. The diversity and annotation richness of ImageNet make it a valuable dataset for developing robust and generalizable deep learning models. More about this organization can be found in the [Dataset Structure](#dataset-structure) section.
+
+### What role does the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) play in computer vision?
+
+The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/) has been pivotal in driving advancements in computer vision by providing a competitive platform for evaluating algorithms on a large-scale, standardized dataset. It offers standardized evaluation metrics, fostering innovation and development in areas such as image classification, object detection, and [image segmentation](https://www.ultralytics.com/glossary/image-segmentation). The challenge has continuously pushed the boundaries of what is possible with deep learning and computer vision technologies.

ultralytics-main/docs/en/datasets/classify/imagenet10.md

@@ -0,0 +1,129 @@
+---
+comments: true
+description: Discover ImageNet10 a compact version of ImageNet for rapid model testing and CI checks. Perfect for quick evaluations in computer vision tasks.
+keywords: ImageNet10, ImageNet, Ultralytics, CI tests, sanity checks, training pipelines, computer vision, deep learning, dataset
+---
+
+# ImageNet10 Dataset
+
+The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://www.ultralytics.com/) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset.
+
+## Key Features
+
+- ImageNet10 is a compact version of ImageNet, with 20 images representing the first 10 classes of the original dataset.
+- The dataset is organized according to the WordNet hierarchy, mirroring the structure of the full ImageNet dataset.
+- It is ideally suited for CI tests, sanity checks, and rapid testing of training pipelines in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
+- Although not designed for model benchmarking, it can provide a quick indication of a model's basic functionality and correctness.
+
+## Dataset Structure
+
+The ImageNet10 dataset, like the original [ImageNet](../classify/imagenet.md), is organized using the WordNet hierarchy. Each of the 10 classes in ImageNet10 is described by a synset (a collection of synonymous terms). The images in ImageNet10 are annotated with one or more synsets, providing a compact resource for testing models to recognize various objects and their relationships.
+
+## Applications
+
+The ImageNet10 dataset is useful for quickly testing and debugging computer vision models and pipelines. Its small size allows for rapid iteration, making it ideal for [continuous integration](../../help/CI.md) tests and sanity checks. It can also be used for fast preliminary testing of new models or changes to existing models before moving on to full-scale testing with the complete [ImageNet dataset](../classify/imagenet.md).
+
+## Usage
+
+To test a deep learning model on the ImageNet10 dataset with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Test Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="imagenet10", epochs=5, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
+        ```
+
+## Sample Images and Annotations
+
+The ImageNet10 dataset contains a subset of images from the original ImageNet dataset. These images are chosen to represent the first 10 classes in the dataset, providing a diverse yet compact dataset for quick testing and evaluation.
+
+![Dataset sample images](https://github.com/ultralytics/docs/releases/download/0/imagenet10-sample-images.avif)
+
+The example showcases the variety and complexity of the images in the ImageNet10 dataset, highlighting its usefulness for sanity checks and quick testing of computer vision models.
+
+## Citations and Acknowledgments
+
+If you use the ImageNet10 dataset in your research or development work, please cite the original ImageNet paper:
+
+!!! quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{ILSVRC15,
+                 author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
+                 title={ImageNet Large Scale Visual Recognition Challenge},
+                 year={2015},
+                 journal={International Journal of Computer Vision (IJCV)},
+                 volume={115},
+                 number={3},
+                 pages={211-252}
+        }
+        ```
+
+We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
+
+## FAQ
+
+### What is the ImageNet10 dataset and how is it different from the full ImageNet dataset?
+
+The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a compact subset of the original [ImageNet](https://www.image-net.org/) database, created by Ultralytics for rapid CI tests, sanity checks, and training pipeline evaluations. ImageNet10 comprises only 20 images, representing the first image in the training and validation sets of the first 10 classes in ImageNet. Despite its small size, it maintains the structure and diversity of the full dataset, making it ideal for quick testing but not for benchmarking models.
+
+### How can I use the ImageNet10 dataset to test my deep learning model?
+
+To test your deep learning model on the ImageNet10 dataset with an image size of 224x224, use the following code snippets.
+
+!!! example "Test Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="imagenet10", epochs=5, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=imagenet10 model=yolo11n-cls.pt epochs=5 imgsz=224
+        ```
+
+Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.
+
+### Why should I use the ImageNet10 dataset for CI tests and sanity checks?
+
+The ImageNet10 dataset is designed specifically for CI tests, sanity checks, and quick evaluations in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) pipelines. Its small size allows for rapid iteration and testing, making it perfect for continuous integration processes where speed is crucial. By maintaining the structural complexity and diversity of the original ImageNet dataset, ImageNet10 provides a reliable indication of a model's basic functionality and correctness without the overhead of processing a large dataset.
+
+### What are the main features of the ImageNet10 dataset?
+
+The ImageNet10 dataset has several key features:
+
+- **Compact Size**: With only 20 images, it allows for rapid testing and debugging.
+- **Structured Organization**: Follows the WordNet hierarchy, similar to the full ImageNet dataset.
+- **CI and Sanity Checks**: Ideally suited for continuous integration tests and sanity checks.
+- **Not for Benchmarking**: While useful for quick model evaluations, it is not designed for extensive benchmarking.
+
+### How does ImageNet10 compare to other small datasets like ImageNette?
+
+While both [ImageNet10](imagenet10.md) and [ImageNette](imagenette.md) are subsets of ImageNet, they serve different purposes. ImageNet10 contains just 20 images (2 per class) from the first 10 classes of ImageNet, making it extremely lightweight for CI testing and quick sanity checks. In contrast, ImageNette contains thousands of images across 10 easily distinguishable classes, making it more suitable for actual model training and development. ImageNet10 is designed for verification of pipeline functionality, while ImageNette is better for meaningful but faster-than-full-ImageNet training experiments.

ultralytics-main/docs/en/datasets/classify/imagenette.md

@@ -0,0 +1,193 @@
+---
+comments: true
+description: Explore the ImageNette dataset, a subset of ImageNet with 10 classes for efficient training and evaluation of image classification models. Ideal for ML and CV projects.
+keywords: ImageNette dataset, ImageNet subset, image classification, machine learning, deep learning, YOLO, Convolutional Neural Networks, ML dataset, education, training
+---
+
+# ImageNette Dataset
+
+The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [ImageNet](https://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of ImageNet for software development and education.
+
+## Key Features
+
+- ImageNette contains images from 10 different classes such as tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute.
+- The dataset comprises colored images of varying dimensions.
+- ImageNette is widely used for training and testing in the field of machine learning, especially for image classification tasks.
+
+## Dataset Structure
+
+The ImageNette dataset is split into two subsets:
+
+1. **Training Set**: This subset contains several thousands of images used for training machine learning models. The exact number varies per class.
+2. **Validation Set**: This subset consists of several hundreds of images used for validating and benchmarking the trained models. Again, the exact number varies per class.
+
+## Applications
+
+The ImageNette dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), and various other machine learning algorithms. The dataset's straightforward format and well-chosen classes make it a handy resource for both beginner and experienced practitioners in the field of [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
+
+## Usage
+
+To train a model on the ImageNette dataset for 100 epochs with a standard image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="imagenette", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
+        ```
+
+## Sample Images and Annotations
+
+The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for [image classification](https://www.ultralytics.com/glossary/image-classification) tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/imagenette-sample-image.avif)
+
+The example showcases the variety and complexity of the images in the ImageNette dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## ImageNette160 and ImageNette320
+
+For faster prototyping and training, the ImageNette dataset is also available in two reduced sizes: [ImageNette160](https://github.com/fastai/imagenette) and [ImageNette320](https://github.com/fastai/imagenette). These datasets maintain the same classes and structure as the full ImageNette dataset, but the images are resized to a smaller dimension. As such, these versions of the dataset are particularly useful for preliminary model testing, or when computational resources are limited.
+
+To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imagenette320' in the training command. The following code snippets illustrate this:
+
+!!! example "Train Example with ImageNette160"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model with ImageNette160
+        results = model.train(data="imagenette160", epochs=100, imgsz=160)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model with ImageNette160
+        yolo classify train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
+        ```
+
+!!! example "Train Example with ImageNette320"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model with ImageNette320
+        results = model.train(data="imagenette320", epochs=100, imgsz=320)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model with ImageNette320
+        yolo classify train data=imagenette320 model=yolo11n-cls.pt epochs=100 imgsz=320
+        ```
+
+These smaller versions of the dataset allow for rapid iterations during the development process while still providing valuable and realistic image classification tasks.
+
+## Citations and Acknowledgments
+
+If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette).
+
+## FAQ
+
+### What is the ImageNette dataset?
+
+The [ImageNette dataset](https://github.com/fastai/imagenette) is a simplified subset of the larger [ImageNet dataset](https://www.image-net.org/), featuring only 10 easily distinguishable classes such as tench, English springer, and French horn. It was created to offer a more manageable dataset for efficient training and evaluation of image classification models. This dataset is particularly useful for quick software development and educational purposes in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision.
+
+### How can I use the ImageNette dataset for training a YOLO model?
+
+To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following commands. Make sure to have the Ultralytics YOLO environment set up.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="imagenette", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224
+        ```
+
+For more details, see the [Training](../../modes/train.md) documentation page.
+
+### Why should I use ImageNette for image classification tasks?
+
+The ImageNette dataset is advantageous for several reasons:
+
+- **Quick and Simple**: It contains only 10 classes, making it less complex and time-consuming compared to larger datasets.
+- **Educational Use**: Ideal for learning and teaching the basics of image classification since it requires less computational power and time.
+- **Versatility**: Widely used to train and benchmark various machine learning models, especially in image classification.
+
+For more details on model training and dataset management, explore the [Dataset Structure](#dataset-structure) section.
+
+### Can the ImageNette dataset be used with different image sizes?
+
+Yes, the ImageNette dataset is also available in two resized versions: ImageNette160 and ImageNette320. These versions help in faster prototyping and are especially useful when computational resources are limited.
+
+!!! example "Train Example with ImageNette160"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")
+
+        # Train the model with ImageNette160
+        results = model.train(data="imagenette160", epochs=100, imgsz=160)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model with ImageNette160
+        yolo classify train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160
+        ```
+
+For more information, refer to [Training with ImageNette160 and ImageNette320](#imagenette160-and-imagenette320).
+
+### What are some practical applications of the ImageNette dataset?
+
+The ImageNette dataset is extensively used in:
+
+- **Educational Settings**: To educate beginners in machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
+- **Software Development**: For rapid prototyping and development of image classification models.
+- **Deep Learning Research**: To evaluate and benchmark the performance of various deep learning models, especially Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs).
+
+Explore the [Applications](#applications) section for detailed use cases.

ultralytics-main/docs/en/datasets/classify/imagewoof.md

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+---
+comments: true
+description: Explore the ImageWoof dataset, a challenging subset of ImageNet focusing on 10 dog breeds, designed to enhance image classification models. Learn more on Ultralytics Docs.
+keywords: ImageWoof dataset, ImageNet subset, dog breeds, image classification, deep learning, machine learning, Ultralytics, training dataset, noisy labels
+---
+
+# ImageWoof Dataset
+
+The [ImageWoof](https://github.com/fastai/imagenette) dataset is a subset of the [ImageNet](imagenet.md) consisting of 10 classes that are challenging to classify, since they're all dog breeds. It was created as a more difficult task for [image classification](https://www.ultralytics.com/glossary/image-classification) algorithms to solve, aiming at encouraging development of more advanced models.
+
+## Key Features
+
+- ImageWoof contains images of 10 different dog breeds: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, and Old English sheepdog.
+- The dataset provides images at various resolutions (full size, 320px, 160px), accommodating for different computational capabilities and research needs.
+- It also includes a version with noisy labels, providing a more realistic scenario where labels might not always be reliable.
+
+## Dataset Structure
+
+The ImageWoof dataset structure is based on the dog breed classes, with each breed having its own directory of images. Similar to other classification datasets, it follows a split-directory format with separate folders for training and validation sets.
+
+## Applications
+
+The ImageWoof dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, especially when it comes to more complex and similar classes. The dataset's challenge lies in the subtle differences between the dog breeds, pushing the limits of model's performance and generalization. It's particularly valuable for:
+
+- Benchmarking classification model performance on fine-grained categories
+- Testing model robustness against similar-looking classes
+- Developing algorithms that can distinguish subtle visual differences
+- Evaluating transfer learning capabilities from general to specific domains
+
+## Usage
+
+To train a CNN model on the ImageWoof dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="imagewoof", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
+        ```
+
+## Dataset Variants
+
+ImageWoof dataset comes in three different sizes to accommodate various research needs and computational capabilities:
+
+1. **Full Size (imagewoof)**: This is the original version of the ImageWoof dataset. It contains full-sized images and is ideal for final training and performance benchmarking.
+
+2. **Medium Size (imagewoof320)**: This version contains images resized to have a maximum edge length of 320 pixels. It's suitable for faster training without significantly sacrificing model performance.
+
+3. **Small Size (imagewoof160)**: This version contains images resized to have a maximum edge length of 160 pixels. It's designed for rapid prototyping and experimentation where training speed is a priority.
+
+To use these variants in your training, simply replace 'imagewoof' in the dataset argument with 'imagewoof320' or 'imagewoof160'. For example:
+
+!!! example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # For medium-sized dataset
+        model.train(data="imagewoof320", epochs=100, imgsz=224)
+
+        # For small-sized dataset
+        model.train(data="imagewoof160", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Load a pretrained model and train on the medium-sized dataset
+        yolo classify train model=yolo11n-cls.pt data=imagewoof320 epochs=100 imgsz=224
+        ```
+
+It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.
+
+## Sample Images and Annotations
+
+The ImageWoof dataset contains colorful images of various dog breeds, providing a challenging dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/imagewoof-dataset-sample.avif)
+
+The example showcases the subtle differences and similarities among the different dog breeds in the ImageWoof dataset, highlighting the complexity and difficulty of the classification task.
+
+## Citations and Acknowledgments
+
+If you use the ImageWoof dataset in your research or development work, please make sure to acknowledge the creators of the dataset by linking to the [official dataset repository](https://github.com/fastai/imagenette).
+
+We would like to acknowledge the [FastAI](https://www.fast.ai/) team for creating and maintaining the ImageWoof dataset as a valuable resource for the [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) research community. For more information about the ImageWoof dataset, visit the [ImageWoof dataset repository](https://github.com/fastai/imagenette).
+
+## FAQ
+
+### What is the ImageWoof dataset in Ultralytics?
+
+The [ImageWoof](https://github.com/fastai/imagenette) dataset is a challenging subset of ImageNet focusing on 10 specific dog breeds. Created to push the limits of image classification models, it features breeds like Beagle, Shih-Tzu, and Golden Retriever. The dataset includes images at various resolutions (full size, 320px, 160px) and even noisy labels for more realistic training scenarios. This complexity makes ImageWoof ideal for developing more advanced deep learning models.
+
+### How can I train a model using the ImageWoof dataset with Ultralytics YOLO?
+
+To train a [Convolutional Neural Network](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNN) model on the ImageWoof dataset using Ultralytics YOLO for 100 epochs at an image size of 224x224, you can use the following code:
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        model = YOLO("yolo11n-cls.pt")  # Load a pretrained model
+        results = model.train(data="imagewoof", epochs=100, imgsz=224)
+        ```
+
+
+    === "CLI"
+
+        ```bash
+        yolo classify train data=imagewoof model=yolo11n-cls.pt epochs=100 imgsz=224
+        ```
+
+For more details on available training arguments, refer to the [Training](../../modes/train.md) page.
+
+### What versions of the ImageWoof dataset are available?
+
+The ImageWoof dataset comes in three sizes:
+
+1. **Full Size (imagewoof)**: Ideal for final training and benchmarking, containing full-sized images.
+2. **Medium Size (imagewoof320)**: Resized images with a maximum edge length of 320 pixels, suited for faster training.
+3. **Small Size (imagewoof160)**: Resized images with a maximum edge length of 160 pixels, perfect for rapid prototyping.
+
+Use these versions by replacing 'imagewoof' in the dataset argument accordingly. Note, however, that smaller images may yield lower classification [accuracy](https://www.ultralytics.com/glossary/accuracy) but can be useful for quicker iterations.
+
+### How do noisy labels in the ImageWoof dataset benefit training?
+
+Noisy labels in the ImageWoof dataset simulate real-world conditions where labels might not always be accurate. Training models with this data helps develop robustness and generalization in image classification tasks. This prepares the models to handle ambiguous or mislabeled data effectively, which is often encountered in practical applications.
+
+### What are the key challenges of using the ImageWoof dataset?
+
+The primary challenge of the ImageWoof dataset lies in the subtle differences among the dog breeds it includes. Since it focuses on 10 closely related breeds, distinguishing between them requires more advanced and fine-tuned image classification models. This makes ImageWoof an excellent benchmark to test the capabilities and improvements of [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models.

ultralytics-main/docs/en/datasets/classify/index.md

@@ -0,0 +1,210 @@
+---
+comments: true
+description: Learn how to structure datasets for YOLO classification tasks. Detailed folder structure and usage examples for effective training.
+keywords: YOLO, image classification, dataset structure, CIFAR-10, Ultralytics, machine learning, training data, model evaluation
+---
+
+# Image Classification Datasets Overview
+
+## Dataset Structure for YOLO Classification Tasks
+
+For [Ultralytics](https://www.ultralytics.com/) YOLO classification tasks, the dataset must be organized in a specific split-directory structure under the `root` directory to facilitate proper training, testing, and optional validation processes. This structure includes separate directories for training (`train`) and testing (`test`) phases, with an optional directory for validation (`val`).
+
+Each of these directories should contain one subdirectory for each class in the dataset. The subdirectories are named after the corresponding class and contain all the images for that class. Ensure that each image file is named uniquely and stored in a common format such as JPEG or PNG.
+
+### Folder Structure Example
+
+Consider the [CIFAR-10](cifar10.md) dataset as an example. The folder structure should look like this:
+
+```
+cifar-10-/
+|
+|-- train/
+|   |-- airplane/
+|   |   |-- 10008_airplane.png
+|   |   |-- 10009_airplane.png
+|   |   |-- ...
+|   |
+|   |-- automobile/
+|   |   |-- 1000_automobile.png
+|   |   |-- 1001_automobile.png
+|   |   |-- ...
+|   |
+|   |-- bird/
+|   |   |-- 10014_bird.png
+|   |   |-- 10015_bird.png
+|   |   |-- ...
+|   |
+|   |-- ...
+|
+|-- test/
+|   |-- airplane/
+|   |   |-- 10_airplane.png
+|   |   |-- 11_airplane.png
+|   |   |-- ...
+|   |
+|   |-- automobile/
+|   |   |-- 100_automobile.png
+|   |   |-- 101_automobile.png
+|   |   |-- ...
+|   |
+|   |-- bird/
+|   |   |-- 1000_bird.png
+|   |   |-- 1001_bird.png
+|   |   |-- ...
+|   |
+|   |-- ...
+|
+|-- val/ (optional)
+|   |-- airplane/
+|   |   |-- 105_airplane.png
+|   |   |-- 106_airplane.png
+|   |   |-- ...
+|   |
+|   |-- automobile/
+|   |   |-- 102_automobile.png
+|   |   |-- 103_automobile.png
+|   |   |-- ...
+|   |
+|   |-- bird/
+|   |   |-- 1045_bird.png
+|   |   |-- 1046_bird.png
+|   |   |-- ...
+|   |
+|   |-- ...
+```
+
+This structured approach ensures that the model can effectively learn from well-organized classes during the training phase and accurately evaluate performance during testing and validation phases.
+
+## Usage
+
+!!! example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
+        ```
+
+!!! tip
+
+    Most built-in dataset names (for example `cifar10`, `imagenette`, or `mnist160`) will automatically download and cache the data the first time you reference them. Point `data` to a folder path only when you have curated a custom dataset.
+
+## Supported Datasets
+
+Ultralytics supports the following datasets with automatic download:
+
+- [Caltech 101](caltech101.md): A dataset containing images of 101 object categories for [image classification](https://www.ultralytics.com/glossary/image-classification) tasks.
+- [Caltech 256](caltech256.md): An extended version of Caltech 101 with 256 object categories and more challenging images.
+- [CIFAR-10](cifar10.md): A dataset of 60K 32x32 color images in 10 classes, with 6K images per class.
+- [CIFAR-100](cifar100.md): An extended version of CIFAR-10 with 100 object categories and 600 images per class.
+- [Fashion-MNIST](fashion-mnist.md): A dataset consisting of 70,000 grayscale images of 10 fashion categories for image classification tasks.
+- [ImageNet](imagenet.md): A large-scale dataset for [object detection](https://www.ultralytics.com/glossary/object-detection) and image classification with over 14 million images and 20,000 categories.
+- [ImageNet-10](imagenet10.md): A smaller subset of ImageNet with 10 categories for faster experimentation and testing.
+- [Imagenette](imagenette.md): A smaller subset of ImageNet that contains 10 easily distinguishable classes for quicker training and testing.
+- [Imagewoof](imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
+- [MNIST](mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
+- [MNIST160](mnist.md): First 8 images of each MNIST category from the MNIST dataset. Dataset contains 160 images total.
+
+### Adding your own dataset
+
+If you have your own dataset and would like to use it for training classification models with Ultralytics YOLO, ensure that it follows the format specified above under "Dataset Structure" and then point your `data` argument to the dataset directory when initializing your training script.
+
+## FAQ
+
+### How do I structure my dataset for YOLO classification tasks?
+
+To structure your dataset for Ultralytics YOLO classification tasks, you should follow a specific split-directory format. Organize your dataset into separate directories for `train`, `test`, and optionally `val`. Each of these directories should contain subdirectories named after each class, with the corresponding images inside. This facilitates smooth training and evaluation processes. For an example, consider the [CIFAR-10](cifar10.md) dataset format:
+
+```
+cifar-10-/
+|-- train/
+|   |-- airplane/
+|   |-- automobile/
+|   |-- bird/
+|   ...
+|-- test/
+|   |-- airplane/
+|   |-- automobile/
+|   |-- bird/
+|   ...
+|-- val/ (optional)
+|   |-- airplane/
+|   |-- automobile/
+|   |-- bird/
+|   ...
+```
+
+For more details, visit the [Dataset Structure for YOLO Classification Tasks](#dataset-structure-for-yolo-classification-tasks) section.
+
+### What datasets are supported by Ultralytics YOLO for image classification?
+
+Ultralytics YOLO supports automatic downloading of several datasets for image classification, including [Caltech 101](caltech101.md), [Caltech 256](caltech256.md), [CIFAR-10](cifar10.md), [CIFAR-100](cifar100.md), [Fashion-MNIST](fashion-mnist.md), [ImageNet](imagenet.md), [ImageNet-10](imagenet10.md), [Imagenette](imagenette.md), [Imagewoof](imagewoof.md), and [MNIST](mnist.md). These datasets are structured in a way that makes them easy to use with YOLO. Each dataset's page provides further details about its structure and applications.
+
+### How do I add my own dataset for YOLO image classification?
+
+To use your own dataset with Ultralytics YOLO, ensure it follows the specified directory format required for the classification task, with separate `train`, `test`, and optionally `val` directories, and subdirectories for each class containing the respective images. Once your dataset is structured correctly, point the `data` argument to your dataset's root directory when initializing the training script. Here's an example in Python:
+
+```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
+
+# Train the model
+results = model.train(data="path/to/your/dataset", epochs=100, imgsz=640)
+```
+
+More details can be found in the [Adding your own dataset](#adding-your-own-dataset) section.
+
+### Why should I use Ultralytics YOLO for image classification?
+
+Ultralytics YOLO offers several benefits for image classification, including:
+
+- **Pretrained Models**: Load pretrained models like `yolo11n-cls.pt` to jump-start your training process.
+- **Ease of Use**: Simple API and CLI commands for training and evaluation.
+- **High Performance**: State-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, ideal for real-time applications.
+- **Support for Multiple Datasets**: Seamless integration with various popular datasets like [CIFAR-10](cifar10.md), [ImageNet](imagenet.md), and more.
+- **Community and Support**: Access to extensive documentation and an active community for troubleshooting and improvements.
+
+For additional insights and real-world applications, you can explore [Ultralytics YOLO](https://www.ultralytics.com/yolo).
+
+### How can I train a model using Ultralytics YOLO?
+
+Training a model using Ultralytics YOLO can be done easily in both Python and CLI. Here's an example:
+
+!!! example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolo11n-cls.pt")  # load a pretrained model
+
+        # Train the model
+        results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo classify train data=path/to/data model=yolo11n-cls.pt epochs=100 imgsz=640
+        ```
+
+These examples demonstrate the straightforward process of training a YOLO model using either approach. For more information, visit the [Usage](#usage) section and the [Train](https://docs.ultralytics.com/tasks/classify/#train) page for classification tasks.