Upload folder using huggingface_hub

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/.name

@@ -0,0 +1 @@
+app.py

.idea/Brain Tumour Detection.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,12 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="PySimpleGUI.theme" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Brain Tumour Detection.iml" filepath="$PROJECT_DIR$/.idea/Brain Tumour Detection.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/ultralytics" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/yolov10" vcs="Git" />
+  </component>
+</project>

.idea/workspace.xml

@@ -0,0 +1,58 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="AutoImportSettings">
+    <option name="autoReloadType" value="SELECTIVE" />
+  </component>
+  <component name="ChangeListManager">
+    <list default="true" id="a00cec43-ea7e-44c4-982a-0a3b53cb6483" name="Changes" comment="" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="FileTemplateManagerImpl">
+    <option name="RECENT_TEMPLATES">
+      <list>
+        <option value="Python Script" />
+      </list>
+    </option>
+  </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$/yolov10" />
+  </component>
+  <component name="ProjectColorInfo">{
+  &quot;associatedIndex&quot;: 3
+}</component>
+  <component name="ProjectId" id="2lC0Vj1vunwOmRgkbP5L9Q37LQE" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">{
+  &quot;keyToString&quot;: {
+    &quot;Python.app.executor&quot;: &quot;Run&quot;,
+    &quot;RunOnceActivity.OpenProjectViewOnStart&quot;: &quot;true&quot;,
+    &quot;RunOnceActivity.ShowReadmeOnStart&quot;: &quot;true&quot;,
+    &quot;git-widget-placeholder&quot;: &quot;main&quot;,
+    &quot;last_opened_file_path&quot;: &quot;C:/Users/suhri/Documents/Brain Tumour Detection&quot;
+  }
+}</component>
+  <component name="SharedIndexes">
+    <attachedChunks>
+      <set>
+        <option value="bundled-python-sdk-5a2391486177-d3b881c8e49f-com.jetbrains.pycharm.community.sharedIndexes.bundled-PC-233.13763.11" />
+      </set>
+    </attachedChunks>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="a00cec43-ea7e-44c4-982a-0a3b53cb6483" name="Changes" comment="" />
+      <created>1724668778161</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1724668778161</updated>
+    </task>
+    <servers />
+  </component>
+</project>

README.md

@@ -1,12 +1,6 @@
 ---
-title: Brain Tumour Detection
-emoji: 📚
-colorFrom: pink
-colorTo: red
+title: Brain_Tumour_Detection
+app_file: app.py
 sdk: gradio
 sdk_version: 4.42.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,22 @@
+import gradio as gr
+import numpy as np
+import joblib
+from ultralytics import YOLO
+
+load_model = YOLO("best.pt")
+
+def predict(image):
+  result = load_model.predict(source=image, imgsz = 640, conf = 0.25)
+  annotated_img = result[0].plot()
+  annotated_img = annotated_img[:, :, ::-1]
+  return annotated_img
+
+app = gr.Interface(
+    fn = predict,
+    inputs = gr.Image(type="numpy", label="Upload an image"),
+    outputs = gr.Image(type="numpy", label="Detect Brain Tumor"),
+    title = "Brain Tumor Detection",
+    description="Upload an image. The model will detect and annotate brain tumor."
+)
+
+app.launch(share = True)

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e407241a5f7694b5901d772875845937881b9f1b2f50bb0311eff3e214d88c6d
+size 5751283

requirements.txt

@@ -0,0 +1,3 @@
+ultralytics == 8.2.82
+joblib == 1.4.2
+numpy == 1.26.4

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

@@ -0,0 +1,97 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+name: 🐛 Bug Report
+# title: " "
+description: Problems with Ultralytics YOLO
+labels: [bug, triage]
+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. 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: Many issues are often related to dependency versions and hardware. Please provide the output of `yolo checks` or `ultralytics.checks()` command to help us diagnose the problem.
+      placeholder: |
+        Paste output of `yolo checks` or `ultralytics.checks()` command, i.e.:
+        ```
+        Ultralytics YOLOv8.0.181 🚀 Python-3.11.2 torch-2.0.1 CPU (Apple M2)
+        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 M2
+        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/.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,13 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+blank_issues_enabled: true
+contact_links:
+  - name: 📄 Docs
+    url: https://docs.ultralytics.com/
+    about: Full Ultralytics YOLOv8 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

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

@@ -0,0 +1,52 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+name: 🚀 Feature Request
+description: Suggest a YOLOv8 idea
+# title: " "
+labels: [enhancement]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting a YOLOv8 🚀 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 YOLOv8 [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 YOLOv8?
+    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 YOLOv8 for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the YOLOv8 [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started.
+      options:
+        - label: Yes I'd like to help by submitting a PR!

ultralytics/.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,35 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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/ultralytics/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/ultralytics/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/.github/dependabot.yml

@@ -0,0 +1,27 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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: weekly
+      time: "04:00"
+    open-pull-requests-limit: 10
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies
+
+  - package-ecosystem: github-actions
+    directory: "/.github/workflows"
+    schedule:
+      interval: weekly
+      time: "04:00"
+    open-pull-requests-limit: 5
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies

ultralytics/.github/workflows/ci.yaml

@@ -0,0 +1,348 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# YOLO Continuous Integration (CI) GitHub Actions tests
+
+name: Ultralytics CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+  schedule:
+    - cron: "0 0 * * *" # runs at 00:00 UTC every day
+  workflow_dispatch:
+    inputs:
+      hub:
+        description: "Run HUB"
+        default: false
+        type: boolean
+      benchmarks:
+        description: "Run Benchmarks"
+        default: false
+        type: boolean
+      tests:
+        description: "Run Tests"
+        default: false
+        type: boolean
+      gpu:
+        description: "Run GPU"
+        default: false
+        type: boolean
+      raspberrypi:
+        description: "Run Raspberry Pi"
+        default: false
+        type: boolean
+      conda:
+        description: "Run Conda"
+        default: false
+        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-version: ["3.11"]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip" # caching pip dependencies
+      - name: Install requirements
+        shell: bash # for Windows compatibility
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install -e . --extra-index-url https://download.pytorch.org/whl/cpu
+      - name: Check environment
+        run: |
+          yolo checks
+          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:
+        os: [ubuntu-latest, macos-14]
+        python-version: ["3.11"]
+        model: [yolov8n]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip" # caching pip dependencies
+      - name: Install requirements
+        shell: bash # for Windows compatibility
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install -e ".[export]" "coverage[toml]" --extra-index-url https://download.pytorch.org/whl/cpu
+      - name: Check environment
+        run: |
+          yolo checks
+          pip list
+      - name: Benchmark ClassificationModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.166
+      - 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=0.318
+      - name: Benchmark SegmentationModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.279
+      - name: Benchmark PoseModel
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.183
+      - 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=0.472
+      - name: Benchmark YOLOv10Model
+        shell: bash
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/yolov10n.pt' imgsz=160 verbose=0.178
+      - 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@v4
+        with:
+          flags: Benchmarks
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+      - name: Benchmark Summary
+        run: |
+          cat benchmarks.log
+          echo "$(cat benchmarks.log)" >> $GITHUB_STEP_SUMMARY
+
+  Tests:
+    if: github.event_name != 'workflow_dispatch' || github.event.inputs.tests == 'true'
+    timeout-minutes: 360
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-14]
+        python-version: ["3.11"]
+        torch: [latest]
+        include:
+          - os: ubuntu-latest
+            python-version: "3.8" # torch 1.8.0 requires python >=3.6, <=3.8
+            torch: "1.8.0" # min torch version CI https://pypi.org/project/torchvision/
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: "pip" # caching pip dependencies
+      - name: Install requirements
+        shell: bash # for Windows compatibility
+        run: |
+          # CoreML must be installed before export due to protobuf error from AutoInstall
+          python -m pip install --upgrade pip wheel
+          slow=""
+          torch=""
+          if [ "${{ matrix.torch }}" == "1.8.0" ]; then
+              torch="torch==1.8.0 torchvision==0.9.0"
+          fi
+          if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
+              slow="pycocotools mlflow ray[tune]"
+          fi
+          pip install -e ".[export]" $torch $slow pytest-cov --extra-index-url https://download.pytorch.org/whl/cpu
+      - name: Check environment
+        run: |
+          yolo checks
+          pip list
+      - name: Pytest tests
+        shell: bash # for Windows compatibility
+        run: |
+          slow=""
+          if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
+              slow="--slow"
+          fi
+          pytest $slow --cov=ultralytics/ --cov-report xml tests/
+      - name: Upload Coverage Reports to CodeCov
+        if: github.repository == 'ultralytics/ultralytics' # && matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
+        uses: codecov/codecov-action@v4
+        with:
+          flags: Tests
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+
+  GPU:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name != 'workflow_dispatch' || github.event.inputs.gpu == 'true')
+    timeout-minutes: 360
+    runs-on: gpu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install requirements
+        run: pip install -e . pytest-cov
+      - name: Check environment
+        run: |
+          yolo checks
+          pip list
+      - name: Pytest tests
+        run: |
+          slow=""
+          if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
+            slow="--slow"
+          fi
+          pytest $slow --cov=ultralytics/ --cov-report xml tests/test_cuda.py
+      - name: Upload Coverage Reports to CodeCov
+        uses: codecov/codecov-action@v4
+        with:
+          flags: GPU
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+
+  RaspberryPi:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.raspberrypi == 'true')
+    timeout-minutes: 120
+    runs-on: raspberry-pi
+    steps:
+      - uses: actions/checkout@v4
+      - name: Activate Virtual Environment
+        run: |
+          python3.11 -m venv env
+          source env/bin/activate
+          echo PATH=$PATH >> $GITHUB_ENV
+      - name: Install requirements
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install -e ".[export]" pytest mlflow pycocotools "ray[tune]"
+      - name: Check environment
+        run: |
+          yolo checks
+          pip list
+      - name: Pytest tests
+        run: pytest --slow tests/
+      - name: Benchmark ClassificationModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolov8n-cls.pt' imgsz=160 verbose=0.166
+      - name: Benchmark YOLOWorld DetectionModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolov8s-worldv2.pt' imgsz=160 verbose=0.318
+      - name: Benchmark SegmentationModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolov8n-seg.pt' imgsz=160 verbose=0.267
+      - name: Benchmark PoseModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolov8n-pose.pt' imgsz=160 verbose=0.179
+      - name: Benchmark OBBModel
+        run: python -m ultralytics.cfg.__init__ benchmark model='yolov8n-obb.pt' imgsz=160 verbose=0.472
+      - name: Benchmark Summary
+        run: |
+          cat benchmarks.log
+          echo "$(cat benchmarks.log)" >> $GITHUB_STEP_SUMMARY
+      - 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" &
+
+  Conda:
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.conda == 'true')
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.11"]
+    defaults:
+      run:
+        shell: bash -el {0}
+    steps:
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+          mamba-version: "*"
+          channels: conda-forge,defaults
+          channel-priority: true
+          activate-environment: anaconda-client-env
+      - name: Cleanup toolcache
+        run: |
+          echo "Free space before deletion:"
+          df -h /
+          rm -rf /opt/hostedtoolcache
+          echo "Free space after deletion:"
+          df -h /
+      - name: Install Linux packages
+        run: |
+          # Fix cv2 ImportError: 'libEGL.so.1: cannot open shared object file: No such file or directory'
+          sudo apt-get update
+          sudo apt-get install -y libegl1 libopengl0
+      - name: Install Libmamba
+        run: |
+          conda config --set solver libmamba
+      - name: Install Ultralytics package from conda-forge
+        run: |
+          conda install -c pytorch -c conda-forge pytorch torchvision ultralytics openvino
+      - name: Install pip packages
+        run: |
+          # CoreML must be installed before export due to protobuf error from AutoInstall
+          pip install pytest "coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'"
+      - name: Check environment
+        run: |
+          conda list
+      - name: Test CLI
+        run: |
+          yolo predict model=yolov8n.pt imgsz=320
+          yolo train model=yolov8n.pt data=coco8.yaml epochs=1 imgsz=32
+          yolo val model=yolov8n.pt data=coco8.yaml imgsz=32
+          yolo export model=yolov8n.pt format=torchscript imgsz=160
+      - 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('yolov8n.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
+        run: |
+          git clone https://github.com/ultralytics/ultralytics
+          pytest ultralytics/tests
+
+  Summary:
+    runs-on: ubuntu-latest
+    needs: [HUB, Benchmarks, Tests, GPU, RaspberryPi, Conda] # Add job names that you want to check for failure
+    if: always() # This ensures the job runs even if previous jobs fail
+    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.Conda.result == 'failure' ) && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push')
+        uses: slackapi/slack-github-action@v1.26.0
+        with:
+          payload: |
+            {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"}
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}

ultralytics/.github/workflows/cla.yml

@@ -0,0 +1,44 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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/github-action@v2.5.1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          # Must be repository secret PAT
+          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_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/.github/workflows/codeql.yaml

@@ -0,0 +1,42 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+name: "CodeQL"
+
+on:
+  schedule:
+    - cron: "0 0 1 * *"
+  workflow_dispatch:
+
+jobs:
+  analyze:
+    name: Analyze
+    runs-on: ${{ 'ubuntu-latest' }}
+    permissions:
+      actions: read
+      contents: read
+      security-events: write
+
+    strategy:
+      fail-fast: false
+      matrix:
+        language: ["python"]
+        # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # Initializes the CodeQL tools for scanning.
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@v3
+        with:
+          languages: ${{ matrix.language }}
+          # If you wish to specify custom queries, you can do so here or in a config file.
+          # By default, queries listed here will override any specified in a config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
+          # queries: security-extended,security-and-quality
+
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@v3
+        with:
+          category: "/language:${{matrix.language}}"

ultralytics/.github/workflows/docker.yaml

@@ -0,0 +1,186 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest images on DockerHub https://hub.docker.com/r/ultralytics
+
+name: Publish Docker Images
+
+on:
+  push:
+    branches: [main]
+    paths-ignore:
+      - "docs/**"
+      - "mkdocs.yml"
+  workflow_dispatch:
+    inputs:
+      Dockerfile:
+        type: boolean
+        description: Use Dockerfile
+        default: true
+      Dockerfile-cpu:
+        type: boolean
+        description: Use Dockerfile-cpu
+        default: true
+      Dockerfile-arm64:
+        type: boolean
+        description: Use Dockerfile-arm64
+        default: true
+      Dockerfile-jetson-jetpack6:
+        type: boolean
+        description: Use Dockerfile-jetson-jetpack6
+        default: true
+      Dockerfile-jetson-jetpack5:
+        type: boolean
+        description: Use Dockerfile-jetson-jetpack5
+        default: true
+      Dockerfile-jetson-jetpack4:
+        type: boolean
+        description: Use Dockerfile-jetson-jetpack4
+        default: true
+      Dockerfile-python:
+        type: boolean
+        description: Use Dockerfile-python
+        default: true
+      Dockerfile-conda:
+        type: boolean
+        description: Use Dockerfile-conda
+        default: true
+      push:
+        type: boolean
+        description: Publish all Images to Docker Hub
+
+jobs:
+  docker:
+    if: github.repository == 'ultralytics/ultralytics'
+    name: Push
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      max-parallel: 10
+      matrix:
+        include:
+          - dockerfile: "Dockerfile"
+            tags: "latest"
+            platforms: "linux/amd64"
+          - dockerfile: "Dockerfile-cpu"
+            tags: "latest-cpu"
+            platforms: "linux/amd64"
+          - dockerfile: "Dockerfile-arm64"
+            tags: "latest-arm64"
+            platforms: "linux/arm64"
+          - dockerfile: "Dockerfile-jetson-jetpack6"
+            tags: "latest-jetson-jetpack6"
+            platforms: "linux/arm64"
+          - dockerfile: "Dockerfile-jetson-jetpack5"
+            tags: "latest-jetson-jetpack5"
+            platforms: "linux/arm64"
+          - dockerfile: "Dockerfile-jetson-jetpack4"
+            tags: "latest-jetson-jetpack4"
+            platforms: "linux/arm64"
+          - dockerfile: "Dockerfile-python"
+            tags: "latest-python"
+            platforms: "linux/amd64"
+          # - dockerfile: "Dockerfile-conda"
+          #   tags: "latest-conda"
+          #   platforms: "linux/amd64"
+    steps:
+      - name: Cleanup toolcache
+        # Free up to 10GB of disk space per https://github.com/ultralytics/ultralytics/pull/14894
+        run: |
+          echo "Free space before deletion:"
+          df -h /
+          rm -rf /opt/hostedtoolcache
+          echo "Free space after deletion:"
+          df -h /
+
+      - name: Checkout repo
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0 # copy full .git directory to access full git history in Docker images
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - 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: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Retrieve Ultralytics version
+        id: get_version
+        run: |
+          VERSION=$(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 "exists=true" >> $GITHUB_OUTPUT
+          elif [[ "$MESSAGE" == *"404"* ]]; then
+              echo "Tag $VERSION_TAG does not exist on DockerHub."
+              echo "exists=false" >> $GITHUB_OUTPUT
+          else
+              echo "Unexpected response from DockerHub. Please check manually."
+              echo "exists=false" >> $GITHUB_OUTPUT
+          fi
+        env:
+          VERSION_TAG: ${{ steps.get_version.outputs.version_tag }}
+
+      - name: Build Image
+        if: github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true'
+        uses: nick-invision/retry@v3
+        with:
+          timeout_minutes: 120
+          retry_wait_seconds: 60
+          max_attempts: 2 # retry once
+          command: |
+            docker build \
+            --platform ${{ matrix.platforms }} \
+            -f docker/${{ matrix.dockerfile }} \
+            -t ultralytics/ultralytics:${{ matrix.tags }} \
+            -t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} \
+            .
+
+      - name: Run Tests
+        if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
+        run: docker run ultralytics/ultralytics:${{ matrix.tags }} /bin/bash -c "pip install pytest && pytest tests"
+
+      - name: Run Benchmarks
+        # WARNING: Dockerfile (GPU) error on TF.js export 'module 'numpy' has no attribute 'object'.
+        if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
+        run: docker run ultralytics/ultralytics:${{ matrix.tags }} yolo benchmark model=yolov8n.pt imgsz=160 verbose=0.318
+
+      - name: Push Docker Image with Ultralytics version tag
+        if: (github.event_name == 'push' || (github.event.inputs[matrix.dockerfile] == 'true' && github.event.inputs.push == 'true')) && steps.check_tag.outputs.exists == 'false' && matrix.dockerfile != 'Dockerfile-conda'
+        run: |
+          docker push ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }}
+
+      - name: Push Docker Image with latest tag
+        if: github.event_name == 'push' || (github.event.inputs[matrix.dockerfile] == 'true' && github.event.inputs.push == 'true')
+        run: |
+          docker push ultralytics/ultralytics:${{ matrix.tags }}
+          if [[ "${{ matrix.tags }}" == "latest" ]]; then
+            t=ultralytics/ultralytics:latest-runner
+            docker build -f docker/Dockerfile-runner -t $t .
+            docker push $t
+          fi
+
+      - name: Notify on failure
+        if: github.event_name == 'push' && failure() # do not notify on cancelled() as cancelling is performed by hand
+        uses: slackapi/slack-github-action@v1.26.0
+        with:
+          payload: |
+            {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"}
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}

ultralytics/.github/workflows/docs.yml

@@ -0,0 +1,101 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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_target:
+    branches: [main]
+  workflow_dispatch:
+
+jobs:
+  Docs:
+    if: github.repository == 'ultralytics/ultralytics'
+    runs-on: macos-14
+    steps:
+      - name: Git config
+        run: |
+          git config --global user.name "UltralyticsAssistant"
+          git config --global user.email "web@ultralytics.com"
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+        with:
+          repository: ${{ github.event.pull_request.head.repo.full_name || github.repository }}
+          token: ${{ secrets.GITHUB_TOKEN }}
+          ref: ${{ github.head_ref || github.ref }}
+          fetch-depth: 0
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+          cache: "pip" # caching pip dependencies
+      - name: Install Dependencies
+        run: pip install ruff black tqdm mkdocs-material "mkdocstrings[python]" mkdocs-jupyter mkdocs-redirects mkdocs-ultralytics-plugin mkdocs-macros-plugin
+      - name: Ruff fixes
+        continue-on-error: true
+        run: ruff check --fix --fix-unsafe --select D --ignore=D100,D104,D203,D205,D212,D213,D401,D406,D407,D413 .
+      - name: Update Docs Reference Section and Push Changes
+        if: github.event_name == 'pull_request_target'
+        run: |
+          python docs/build_reference.py
+          git pull origin ${{ github.head_ref || github.ref }}
+          git add .
+          git reset HEAD -- .github/workflows/  # workflow changes are not permitted with default token
+          if ! 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 --select D --ignore=D100,D104,D203,D205,D212,D213,D401,D406,D407,D413 .
+      - name: Build Docs and Check for Warnings
+        run: |
+          export JUPYTER_PLATFORM_DIRS=1
+          python docs/build_docs.py
+      - name: Commit and Push Docs changes
+        continue-on-error: true
+        if: always() && github.event_name == 'pull_request_target'
+        run: |
+          git pull origin ${{ github.head_ref || github.ref }}
+          git add --update  # only add updated files
+          git reset HEAD -- .github/workflows/  # workflow changes are not permitted with default token
+          if ! 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'
+        env:
+          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+          INDEXNOW_KEY: ${{ secrets.INDEXNOW_KEY_DOCS }}
+        run: |
+          git clone https://github.com/ultralytics/docs.git docs-repo
+          cd docs-repo
+          git checkout gh-pages || git checkout -b gh-pages
+          rm -rf *
+          cp -R ../site/* .
+          echo "$INDEXNOW_KEY" > "$INDEXNOW_KEY.txt"
+          git add .
+          if git diff --staged --quiet; then
+            echo "No changes to commit"
+          else
+            LATEST_HASH=$(git rev-parse --short=7 HEAD)
+            git commit -m "Update Docs for 'ultralytics ${{ steps.check_pypi.outputs.version }} - $LATEST_HASH'"
+            git push https://$PERSONAL_ACCESS_TOKEN@github.com/ultralytics/docs.git gh-pages
+          fi

ultralytics/.github/workflows/format.yml

@@ -0,0 +1,29 @@
+# Ultralytics 🚀 - AGPL-3.0 License https://ultralytics.com/license
+# Ultralytics Actions https://github.com/ultralytics/actions
+# This workflow automatically formats code and documentation in PRs to official Ultralytics standards
+
+name: Ultralytics Actions
+
+on:
+  issues:
+    types: [opened, edited]
+  pull_request_target:
+    branches: [main]
+    types: [opened, closed, synchronize, review_requested]
+
+jobs:
+  format:
+    runs-on: macos-14
+    steps:
+      - name: Run Ultralytics Formatting
+        uses: ultralytics/actions@main
+        with:
+          token: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated
+          labels: true # autolabel issues and PRs
+          python: true # format Python code and docstrings
+          prettier: true # format YAML, JSON, Markdown and CSS
+          spelling: true # check spelling
+          links: false # check broken links
+          summary: true # print PR summary with GPT4o (requires 'openai_api_key')
+          openai_azure_api_key: ${{ secrets.OPENAI_AZURE_API_KEY }}
+          openai_azure_endpoint: ${{ secrets.OPENAI_AZURE_ENDPOINT }}

ultralytics/.github/workflows/greetings.yml

@@ -0,0 +1,58 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+name: Greetings
+
+on:
+  pull_request_target:
+    types: [opened]
+  issues:
+    types: [opened]
+
+jobs:
+  greeting:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/first-interaction@v1
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          pr-message: |
+            👋 Hello @${{ github.actor }}, thank you for submitting an Ultralytics YOLOv8 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+            - ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
+            - ✅ Verify all YOLOv8 Continuous Integration (CI) **checks are passing**.
+            - ✅ Update YOLOv8 [Docs](https://docs.ultralytics.com) for any new or updated features.
+            - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
+
+            See our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for details and let us know if you have any questions!
+
+          issue-message: |
+            👋 Hello @${{ github.actor }}, thank you for your interest in Ultralytics YOLOv8 🚀! 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 vibrant [Ultralytics Discord](https://ultralytics.com/discord) 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
+
+            ## Install
+
+            Pip install the `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+            ```bash
+            pip install ultralytics
+            ```
+
+            ## Environments
+
+            YOLOv8 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/ultralytics/yolov8"><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.yaml?query=event%3Aschedule"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+
+            If this badge is green, all [Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule) tests are currently passing. CI tests verify correct operation of all YOLOv8 [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/.github/workflows/links.yml

@@ -0,0 +1,93 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0 * * *" # runs at 00:00 UTC every day
+
+jobs:
+  Links:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Download and install lychee
+        run: |
+          LYCHEE_URL=$(curl -s https://api.github.com/repos/lycheeverse/lychee/releases/latest | grep "browser_download_url" | grep "x86_64-unknown-linux-gnu.tar.gz" | cut -d '"' -f 4)
+          curl -L $LYCHEE_URL -o lychee.tar.gz
+          tar xzf lychee.tar.gz
+          sudo mv lychee /usr/local/bin
+
+      - name: Test Markdown and HTML links with retry
+        uses: nick-invision/retry@v3
+        with:
+          timeout_minutes: 5
+          retry_wait_seconds: 60
+          max_attempts: 3
+          command: |
+            lychee \
+            --scheme https \
+            --timeout 60 \
+            --insecure \
+            --accept 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'
+
+      - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
+        if: github.event_name == 'workflow_dispatch'
+        uses: nick-invision/retry@v3
+        with:
+          timeout_minutes: 5
+          retry_wait_seconds: 60
+          max_attempts: 3
+          command: |
+            lychee \
+            --scheme https \
+            --timeout 60 \
+            --insecure \
+            --accept 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.yaml' \
+            --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'

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

@@ -0,0 +1,91 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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 }}
+
+jobs:
+  Merge:
+    if: github.repository == 'ultralytics/ultralytics'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v5
+        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(os.getenv('GITHUB_TOKEN'))
+          repo = g.get_repo(os.getenv('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}")
+
+        env:
+          GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }}
+          GITHUB_REPOSITORY: ${{ github.repository }}

ultralytics/.github/workflows/publish.yml

@@ -0,0 +1,206 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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:
+  publish:
+    if: github.repository == 'ultralytics/ultralytics' && github.actor == 'glenn-jocher'
+    name: Publish
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          token: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }} # use your PAT here
+      - name: Git config
+        run: |
+          git config --global user.name "UltralyticsAssistant"
+          git config --global user.email "web@ultralytics.com"
+      - name: Set up Python environment
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+          cache: "pip" # caching pip dependencies
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install openai requests build twine toml
+      - name: Check PyPI version
+        shell: python
+        run: |
+          import os
+          import requests
+          import toml
+
+          # Load version and package name from pyproject.toml
+          pyproject = toml.load('pyproject.toml')
+          package_name = pyproject['project']['name']
+          local_version = pyproject['project'].get('version', 'dynamic')
+
+          # If version is dynamic, extract it from the specified file
+          if local_version == 'dynamic':
+              version_attr = pyproject['tool']['setuptools']['dynamic']['version']['attr']
+              module_path, attr_name = version_attr.rsplit('.', 1)
+              with open(f"{module_path.replace('.', '/')}/__init__.py") as f:
+                  local_version = next(line.split('=')[1].strip().strip("'\"") for line in f if line.startswith(attr_name))
+
+          print(f"Local Version: {local_version}")
+
+          # Get online version from PyPI
+          response = requests.get(f"https://pypi.org/pypi/{package_name}/json")
+          online_version = response.json()['info']['version'] if response.status_code == 200 else None
+          print(f"Online Version: {online_version or 'Not Found'}")
+
+          # Determine if a new version should be published
+          publish = False
+          if online_version:
+              local_ver = tuple(map(int, local_version.split('.')))
+              online_ver = tuple(map(int, online_version.split('.')))
+              major_diff = local_ver[0] - online_ver[0]
+              minor_diff = local_ver[1] - online_ver[1]
+              patch_diff = local_ver[2] - online_ver[2]
+
+              publish = (
+                  (major_diff == 0 and minor_diff == 0 and 0 < patch_diff <= 2) or
+                  (major_diff == 0 and minor_diff == 1 and local_ver[2] == 0) or
+                  (major_diff == 1 and local_ver[1] == 0 and local_ver[2] == 0)
+              )
+          else:
+              publish = True  # First release
+
+          os.system(f'echo "increment={publish}" >> $GITHUB_OUTPUT')
+          os.system(f'echo "version={local_version}" >> $GITHUB_OUTPUT')
+          os.system(f'echo "previous_version={online_version or "N/A"}" >> $GITHUB_OUTPUT')
+
+          if publish:
+              print('Ready to publish new version to PyPI ✅.')
+        id: check_pypi
+      - name: Publish new tag
+        if: (github.event_name == 'push' || github.event.inputs.pypi == 'true')  && steps.check_pypi.outputs.increment == 'True'
+        run: |
+          git tag -a "v${{ steps.check_pypi.outputs.version }}" -m "$(git log -1 --pretty=%B)"  # i.e. "v0.1.2 commit message"
+          git push origin "v${{ steps.check_pypi.outputs.version }}"
+      - name: Publish new release
+        if: (github.event_name == 'push' || github.event.inputs.pypi == 'true')  && steps.check_pypi.outputs.increment == 'True'
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }}
+          CURRENT_TAG: ${{ steps.check_pypi.outputs.version }}
+          PREVIOUS_TAG: ${{ steps.check_pypi.outputs.previous_version }}
+        shell: python
+        run: |
+          import openai
+          import os
+          import requests
+          import json
+          import subprocess
+
+          # Retrieve environment variables
+          OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+          GITHUB_TOKEN = os.getenv('GITHUB_TOKEN')
+          CURRENT_TAG = os.getenv('CURRENT_TAG')
+          PREVIOUS_TAG = os.getenv('PREVIOUS_TAG')
+
+          # Check for required environment variables
+          if not all([OPENAI_API_KEY, GITHUB_TOKEN, CURRENT_TAG, PREVIOUS_TAG]):
+              raise ValueError("One or more required environment variables are missing.")
+
+          latest_tag = f"v{CURRENT_TAG}"
+          previous_tag = f"v{PREVIOUS_TAG}"
+          repo = os.getenv('GITHUB_REPOSITORY')
+          headers = {"Authorization": f"token {GITHUB_TOKEN}", "Accept": "application/vnd.github.v3.diff"}
+
+          # Get the diff between the tags
+          url = f"https://api.github.com/repos/{repo}/compare/{previous_tag}...{latest_tag}"
+          response = requests.get(url, headers=headers)
+          diff = response.text if response.status_code == 200 else f"Failed to get diff: {response.content}"
+
+          # Get summary
+          messages = [
+              {
+                  "role": "system",
+                  "content": "You are an Ultralytics AI assistant skilled in software development and technical communication. Your task is to summarize GitHub releases in a way that is detailed, accurate, and understandable to both expert developers and non-expert users. Focus on highlighting the key changes and their impact in simple and intuitive terms."
+              },
+              {
+                  "role": "user",
+                  "content": f"Summarize the updates made in the '{latest_tag}' tag, focusing on major changes, their purpose, and potential impact. Keep the summary clear and suitable for a broad audience. Add emojis to enliven the summary. Reply directly with a summary along these example guidelines, though feel free to adjust as appropriate:\n\n"
+                             f"## 🌟 Summary (single-line synopsis)\n"
+                             f"## 📊 Key Changes (bullet points highlighting any major changes)\n"
+                             f"## 🎯 Purpose & Impact (bullet points explaining any benefits and potential impact to users)\n"
+                             f"\n\nHere's the release diff:\n\n{diff[:300000]}",
+              }
+          ]
+          client = openai.OpenAI(api_key=OPENAI_API_KEY)
+          completion = client.chat.completions.create(model="gpt-4o-2024-08-06", messages=messages)
+          summary = completion.choices[0].message.content.strip()
+
+          # Get the latest commit message
+          commit_message = subprocess.run(['git', 'log', '-1', '--pretty=%B'], check=True, text=True, capture_output=True).stdout.split("\n")[0].strip()
+
+          # Prepare release data
+          release = {
+              'tag_name': latest_tag,
+              'name': f"{latest_tag} - {commit_message}",
+              'body': summary,
+              'draft': False,
+              'prerelease': False
+          }
+
+          # Create the release on GitHub
+          release_url = f"https://api.github.com/repos/{repo}/releases"
+          release_response = requests.post(release_url, headers=headers, data=json.dumps(release))
+          if release_response.status_code == 201:
+              print(f'Successfully created release {latest_tag}')
+          else:
+              print(f'Failed to create release {latest_tag}: {release_response.content}')
+      - name: Publish to PyPI
+        continue-on-error: true
+        if: (github.event_name == 'push' || github.event.inputs.pypi == 'true')  && steps.check_pypi.outputs.increment == 'True'
+        env:
+          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
+        run: |
+          python -m build
+          python -m twine upload dist/* -u __token__ -p $PYPI_TOKEN
+      - name: Extract PR Details
+        env:
+          GH_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }}
+        run: |
+          # Check if the event is a pull request or pull_request_target
+          if [ "${{ github.event_name }}" = "pull_request" ] || [ "${{ github.event_name }}" = "pull_request_target" ]; then
+            PR_NUMBER=${{ github.event.pull_request.number }}
+            PR_TITLE=$(gh pr view $PR_NUMBER --json title --jq '.title')
+          else
+            # Use gh to find the PR associated with the commit
+            COMMIT_SHA=${{ github.event.after }}
+            PR_JSON=$(gh pr list --search "${COMMIT_SHA}" --state merged --json number,title --jq '.[0]')
+            PR_NUMBER=$(echo $PR_JSON | jq -r '.number')
+            PR_TITLE=$(echo $PR_JSON | jq -r '.title')
+          fi
+          echo "PR_NUMBER=$PR_NUMBER" >> $GITHUB_ENV
+          echo "PR_TITLE=$PR_TITLE" >> $GITHUB_ENV
+      - name: Notify on Slack (Success)
+        if: success() && github.event_name == 'push' && steps.check_pypi.outputs.increment == 'True'
+        uses: slackapi/slack-github-action@v1.26.0
+        with:
+          payload: |
+            {"text": "<!channel> GitHub Actions success for ${{ github.workflow }} ✅\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* NEW '${{ github.repository }} v${{ steps.check_pypi.outputs.version }}' pip package published 😃\n*Job Status:* ${{ job.status }}\n*Pull Request:* <https://github.com/${{ github.repository }}/pull/${{ env.PR_NUMBER }}> ${{ env.PR_TITLE }}\n"}
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
+      - name: Notify on Slack (Failure)
+        if: failure()
+        uses: slackapi/slack-github-action@v1.26.0
+        with:
+          payload: |
+            {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n*Job Status:* ${{ job.status }}\n*Pull Request:* <https://github.com/${{ github.repository }}/pull/${{ env.PR_NUMBER }}> ${{ env.PR_TITLE }}\n"}
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}

ultralytics/.github/workflows/stale.yml

@@ -0,0 +1,47 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+name: Close stale issues
+on:
+  schedule:
+    - cron: "0 0 * * *" # Runs at 00:00 UTC every day
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v9
+        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 ⭐
+
+          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"
+          operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.

ultralytics/.gitignore

@@ -0,0 +1,172 @@
+# 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
+
+# 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/
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+mkdocs_github_authors.yaml
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# datasets and projects
+datasets/
+runs/
+wandb/
+.DS_Store
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+weights/
+*.weights
+*.pt
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.mlpackage
+*.torchscript
+*.tflite
+*.h5
+*_saved_model/
+*_web_model/
+*_openvino_model/
+*_paddle_model/
+*_ncnn_model/
+pnnx*
+
+# Autogenerated files for tests
+/ultralytics/assets/
+
+# calibration image
+calibration_*.npy

ultralytics/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'
+  - given-names: Ayush
+    family-names: Chaurasia
+    affiliation: Ultralytics
+    orcid: 'https://orcid.org/0000-0002-7603-6750'
+  - family-names: Qiu
+    given-names: Jing
+    affiliation: Ultralytics
+    orcid: 'https://orcid.org/0000-0003-3783-7069'
+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/CONTRIBUTING.md

@@ -0,0 +1,166 @@
+---
+comments: true
+description: Learn how to contribute to Ultralytics YOLO open-source repositories. Follow guidelines for pull requests, code of conduct, and bug reporting.
+keywords: Ultralytics, YOLO, open-source, contribution, pull request, code of conduct, bug reporting, GitHub, CLA, Google-style docstrings
+---
+
+# Contributing to Ultralytics Open-Source Projects
+
+Welcome! We're thrilled that you're considering contributing to our [Ultralytics](https://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 community. This guide provides clear guidelines and best practices to help you get started.
+
+<a href="https://github.com/ultralytics/ultralytics/graphs/contributors">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
+
+## Table of Contents
+
+1. [Code of Conduct](#code-of-conduct)
+2. [Contributing via Pull Requests](#contributing-via-pull-requests)
+   - [CLA Signing](#cla-signing)
+   - [Google-Style Docstrings](#google-style-docstrings)
+   - [GitHub Actions CI Tests](#github-actions-ci-tests)
+3. [Reporting Bugs](#reporting-bugs)
+4. [License](#license)
+5. [Conclusion](#conclusion)
+6. [FAQ](#faq)
+
+## 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. 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 Ultralytics YOLO repository 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 that reflects your changes.
+
+3. **Make your changes:** Ensure your code adheres to the project's style guidelines and does not introduce any new errors or warnings.
+
+4. **[Test your changes](https://github.com/ultralytics/ultralytics/tree/main/tests):** Before submitting, test your changes locally to confirm they work as expected and don't cause any new issues.
+
+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 a concise and descriptive commit message. If your changes address a specific issue, include the issue number in your commit message.
+
+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 forked repository to the main Ultralytics YOLO repository. Provide a clear and detailed explanation of your changes and how they improve the project.
+
+### 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.
+
+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:
+
+```
+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
+
+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 with type hints
+
+This example includes both a Google-style docstring and type hints 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 [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.
+
+## Reporting Bugs
+
+We highly value bug reports as they help us maintain the quality of our projects. When reporting a bug, please provide a [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum_reproducible_example)—a simple, clear code example that consistently reproduces the issue. This allows us to quickly identify and resolve the problem.
+
+## License
+
+Ultralytics uses the [GNU Affero General Public License v3.0 (AGPL-3.0)](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) for its repositories. This license promotes openness, transparency, and collaborative improvement 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 to contribute effectively and ethically to the Ultralytics open-source community.
+
+## Conclusion
+
+Thank you for your interest in contributing to [Ultralytics](https://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 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:
+
+```
+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.

ultralytics/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
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+software and other kinds of works, specifically designed to ensure
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+
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+
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+
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ultralytics/README.md

@@ -0,0 +1,297 @@
+<div align="center">
+  <p>
+    <a href="https://www.ultralytics.com/events/yolovision" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="YOLO Vision 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.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLOv8 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Ultralytics Docker Pulls"></a>
+    <a href="https://ultralytics.com/discord"><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>
+    <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/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
+</div>
+<br>
+
+[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
+
+We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
+
+To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
+
+<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png" alt="YOLOv8 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://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://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+</div>
+
+## <div align="center">Documentation</div>
+
+See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
+
+<details open>
+<summary>Install</summary>
+
+Pip 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/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/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 Git, please refer to 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)
+
+</details>
+
+<details open>
+<summary>Usage</summary>
+
+### CLI
+
+YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
+
+```bash
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
+
+### Python
+
+YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
+
+```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8n.yaml")  # build a new model from scratch
+model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+# Use the model
+model.train(data="coco8.yaml", epochs=3)  # train the model
+metrics = model.val()  # evaluate model performance on the validation set
+results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
+path = model.export(format="onnx")  # export the model to ONNX format
+```
+
+See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
+
+</details>
+
+### Notebooks
+
+Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://youtube.com/ultralytics?sub_confirmation=1) tutorial, making it easy to learn and implement advanced YOLOv8 features.
+
+| Docs                                                                                                                               | Notebook                                                                                                                                                                                                                   |                                                                                                     YouTube                                                                                                     |
+| ---------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| <a href="https://docs.ultralytics.com/modes/">YOLOv8 Train, Val, Predict and Export Modes</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://youtu.be/j8uQc0qB91s"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/hub/quickstart/">Ultralytics HUB QuickStart</a>                                              | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>                       | <a href="https://youtu.be/lveF9iCMIzc"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/modes/track/">YOLOv8 Multi-Object Tracking in Videos</a>                                     | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>           | <a href="https://youtu.be/hHyHmOtmEgs"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/guides/object-counting/">YOLOv8 Object Counting in Videos</a>                                | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>           | <a href="https://youtu.be/Ag2e-5_NpS0"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/guides/heatmaps/">YOLOv8 Heatmaps in Videos</a>                                              | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>                  | <a href="https://youtu.be/4ezde5-nZZw"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/datasets/explorer/">Ultralytics Datasets Explorer with SQL and OpenAI Integration 🚀 New</a> | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://youtu.be/3VryynorQeo"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+
+## <div align="center">Models</div>
+
+YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
+
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
+
+All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+
+<details open><summary>Detection (COCO)</summary>
+
+See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.
+
+| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Detection (Open Image V7)</summary>
+
+See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/), which include 600 pre-trained classes.
+
+| Model                                                                                     | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-oiv7.pt) | 640                   | 18.4                 | 142.4                          | 1.21                                | 3.5                | 10.5              |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-oiv7.pt) | 640                   | 27.7                 | 183.1                          | 1.40                                | 11.4               | 29.7              |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-oiv7.pt) | 640                   | 33.6                 | 408.5                          | 2.26                                | 26.2               | 80.6              |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640                   | 34.9                 | 596.9                          | 2.43                                | 44.1               | 167.4             |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640                   | 36.3                 | 860.6                          | 3.56                                | 68.7               | 260.6             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/) dataset. <br>Reproduce by `yolo val detect data=open-images-v7.yaml device=0`
+- **Speed** averaged over Open Image V7 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Segmentation (COCO)</summary>
+
+See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), which include 80 pre-trained 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>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
+| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
+| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
+| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
+| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Pose (COCO)</summary>
+
+See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples with these models trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), which include 1 pre-trained class, person.
+
+| 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>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt)       | 640                   | 50.4                  | 80.1               | 131.8                          | 1.18                                | 3.3                | 9.2               |
+| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt)       | 640                   | 60.0                  | 86.2               | 233.2                          | 1.42                                | 11.6               | 30.2              |
+| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |
+| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |
+| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
+| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>OBB (DOTAv1)</summary>
+
+See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
+
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-obb.pt) | 1024                  | 78.0               | 204.77                         | 3.57                                | 3.1                | 23.3              |
+| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-obb.pt) | 1024                  | 79.5               | 424.88                         | 4.07                                | 11.4               | 76.3              |
+| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-obb.pt) | 1024                  | 80.5               | 763.48                         | 7.61                                | 26.4               | 208.6             |
+| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-obb.pt) | 1024                  | 80.7               | 1278.42                        | 11.83                               | 44.5               | 433.8             |
+| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-obb.pt) | 1024                  | 81.36              | 1759.10                        | 13.23                               | 69.5               | 676.7             |
+
+- **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
+- **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Classification (ImageNet)</summary>
+
+See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), which include 1000 pretrained classes.
+
+| Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
+| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
+| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224                   | 69.0             | 88.3             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
+| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224                   | 73.8             | 91.7             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
+| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224                   | 76.8             | 93.5             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
+| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224                   | 78.3             | 94.2             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
+| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224                   | 79.0             | 94.6             | 232.0                          | 1.01                                | 57.4               | 154.8                    |
+
+- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
+- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
+
+</details>
+
+## <div align="center">Integrations</div>
+
+Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow.
+
+<br>
+<a href="https://ultralytics.com/hub" 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://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://clear.ml/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://bit.ly/yolov8-readme-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://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
+</div>
+
+|                                                           Roboflow                                                           |                                                 ClearML ⭐ NEW                                                  |                                                                       Comet ⭐ NEW                                                                        |                                          Neural Magic ⭐ NEW                                           |
+| :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
+
+## <div align="center">Ultralytics HUB</div>
+
+Experience seamless AI with [Ultralytics HUB](https://ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
+
+<a href="https://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>
+
+## <div align="center">Contribute</div>
+
+We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/ultralytics/graphs/contributors">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
+
+## <div align="center">License</div>
+
+Ultralytics offers two licensing options to accommodate diverse use cases:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
+- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license).
+
+## <div align="center">Contact</div>
+
+For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://ultralytics.com/discord) community for questions and discussions!
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><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://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://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>

ultralytics/README.zh-CN.md

@@ -0,0 +1,299 @@
+<div align="center">
+  <p>
+    <a href="https://www.ultralytics.com/events/yolovision" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="YOLO Vision 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.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
+    <a href="https://ultralytics.com/discord"><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>
+    <br>
+    <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/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+</div>
+<br>
+
+[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。
+
+我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 <a href="https://docs.ultralytics.com/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://ultralytics.com/discord">Discord</a> 社区进行问题和讨论！
+
+如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格
+
+<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png" alt="YOLOv8 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://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://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+</div>
+
+以下是提供的内容的中文翻译：
+
+## <div align="center">文档</div>
+
+请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关训练、验证、预测和部署的完整文档。
+
+<details open>
+<summary>安装</summary>
+
+使用Pip在一个[**Python>=3.8**](https://www.python.org/)环境中安装`ultralytics`包，此环境还需包含[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。这也会安装所有必要的[依赖项](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml)。
+
+[![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://pepy.tech/project/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)
+
+```bash
+pip install ultralytics
+```
+
+如需使用包括[Conda](https://anaconda.org/conda-forge/ultralytics)，[Docker](https://hub.docker.com/r/ultralytics/ultralytics)和Git在内的其他安装方法，请参考[快速入门指南](https://docs.ultralytics.com/quickstart)。
+
+[![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)
+
+</details>
+
+<details open>
+<summary>Usage</summary>
+
+### CLI
+
+YOLOv8 可以在命令行界面（CLI）中直接使用，只需输入 `yolo` 命令：
+
+```bash
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+`yolo` 可用于各种任务和模式，并接受其他参数，例如 `imgsz=640`。查看 YOLOv8 [CLI 文档](https://docs.ultralytics.com/usage/cli)以获取示例。
+
+### Python
+
+YOLOv8 也可以在 Python 环境中直接使用，并接受与上述 CLI 示例中相同的[参数](https://docs.ultralytics.com/usage/cfg/)：
+
+```python
+from ultralytics import YOLO
+
+# 加载模型
+model = YOLO("yolov8n.yaml")  # 从头开始构建新模型
+model = YOLO("yolov8n.pt")  # 加载预训练模型（建议用于训练）
+
+# 使用模型
+model.train(data="coco8.yaml", epochs=3)  # 训练模型
+metrics = model.val()  # 在验证集上评估模型性能
+results = model("https://ultralytics.com/images/bus.jpg")  # 对图像进行预测
+success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
+```
+
+查看 YOLOv8 [Python 文档](https://docs.ultralytics.com/usage/python)以获取更多示例。
+
+</details>
+
+### 笔记本
+
+Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟踪等内容。每个笔记本都配有 [YouTube](https://youtube.com/ultralytics?sub_confirmation=1) 教程，使学习和实现高级 YOLOv8 功能变得简单。
+
+| 文档                                                                                                              | 笔记本                                                                                                                                                                                                                       |                                                                                                     YouTube                                                                                                     |
+| ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| <a href="https://docs.ultralytics.com/modes/">YOLOv8 训练、验证、预测和导出模式</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="在 Colab 中打开"></a>                  | <a href="https://youtu.be/j8uQc0qB91s"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a>  |
+| <a href="https://docs.ultralytics.com/hub/quickstart/">Ultralytics HUB 快速开始</a>                               | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>                       | <a href="https://youtu.be/lveF9iCMIzc"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a>  |
+| <a href="https://docs.ultralytics.com/modes/track/">YOLOv8 视频中的多对象跟踪</a>                                 | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>           | <a href="https://youtu.be/hHyHmOtmEgs"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a>  |
+| <a href="https://docs.ultralytics.com/guides/object-counting/">YOLOv8 视频中的对象计数</a>                        | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>           | <a href="https://youtu.be/Ag2e-5_NpS0"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a>  |
+| <a href="https://docs.ultralytics.com/guides/heatmaps/">YOLOv8 视频中的热图</a>                                   | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>                  | <a href="https://youtu.be/4ezde5-nZZw"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a>  |
+| <a href="https://docs.ultralytics.com/datasets/explorer/">Ultralytics 数据集浏览器，集成 SQL 和 OpenAI 🚀 New</a> | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a> | <a href="https://youtu.be/3VryynorQeo"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+
+## <div align="center">模型</div>
+
+在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
+
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
+
+所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时会自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
+
+<details open><summary>检测 (COCO)</summary>
+
+查看[检测文档](https://docs.ultralytics.com/tasks/detect/)以获取这些在[COCO](https://docs.ultralytics.com/datasets/detect/coco/)上训练的模型的使用示例，其中包括80个预训练类别。
+
+| 模型                                                                                 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640                 | 37.3                 | 80.4                          | 0.99                               | 3.2              | 8.7               |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640                 | 44.9                 | 128.4                         | 1.20                               | 11.2             | 28.6              |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640                 | 50.2                 | 234.7                         | 1.83                               | 25.9             | 78.9              |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640                 | 52.9                 | 375.2                         | 2.39                               | 43.7             | 165.2             |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640                 | 53.9                 | 479.1                         | 3.53                               | 68.2             | 257.8             |
+
+- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val detect data=coco.yaml device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现
+
+</details>
+
+<details><summary>检测（Open Image V7）</summary>
+
+查看[检测文档](https://docs.ultralytics.com/tasks/detect/)以获取这些在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)上训练的模型的使用示例，其中包括600个预训练类别。
+
+| 模型                                                                                      | 尺寸<br><sup>(像素) | mAP<sup>验证<br>50-95 | 速度<br><sup>CPU ONNX<br>(毫秒) | 速度<br><sup>A100 TensorRT<br>(毫秒) | 参数<br><sup>(M) | 浮点运算<br><sup>(B) |
+| ----------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------------------- | ------------------------------------ | ---------------- | -------------------- |
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-oiv7.pt) | 640                 | 18.4                  | 142.4                           | 1.21                                 | 3.5              | 10.5                 |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-oiv7.pt) | 640                 | 27.7                  | 183.1                           | 1.40                                 | 11.4             | 29.7                 |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-oiv7.pt) | 640                 | 33.6                  | 408.5                           | 2.26                                 | 26.2             | 80.6                 |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640                 | 34.9                  | 596.9                           | 2.43                                 | 44.1             | 167.4                |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640                 | 36.3                  | 860.6                           | 3.56                                 | 68.7             | 260.6                |
+
+- **mAP<sup>验证</sup>** 值适用于在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)数据集上的单模型单尺度。 <br>通过 `yolo val detect data=open-images-v7.yaml device=0` 以复现。
+- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对Open Image V7验证图像进行平均测算。 <br>通过 `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu` 以复现。
+
+</details>
+
+<details><summary>分割 (COCO)</summary>
+
+查看[分割文档](https://docs.ultralytics.com/tasks/segment/)以获取这些在[COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/)上训练的模型的使用示例，其中包括80个预训练类别。
+
+| 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>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
+| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640                 | 36.7                 | 30.5                  | 96.1                          | 1.21                               | 3.4              | 12.6              |
+| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640                 | 44.6                 | 36.8                  | 155.7                         | 1.47                               | 11.8             | 42.6              |
+| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640                 | 49.9                 | 40.8                  | 317.0                         | 2.18                               | 27.3             | 110.2             |
+| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640                 | 52.3                 | 42.6                  | 572.4                         | 2.79                               | 46.0             | 220.5             |
+| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                 | 53.4                 | 43.4                  | 712.1                         | 4.02                               | 71.8             | 344.1             |
+
+- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val segment data=coco-seg.yaml device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` 复现
+
+</details>
+
+<details><summary>姿态 (COCO)</summary>
+
+查看[姿态文档](https://docs.ultralytics.com/tasks/pose/)以获取这些在[COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/)上训练的模型的使用示例，其中包括1个预训练类别，即人。
+
+| 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>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
+| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt)       | 640                 | 50.4                  | 80.1               | 131.8                         | 1.18                               | 3.3              | 9.2               |
+| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt)       | 640                 | 60.0                  | 86.2               | 233.2                         | 1.42                               | 11.6             | 30.2              |
+| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt)       | 640                 | 65.0                  | 88.8               | 456.3                         | 2.00                               | 26.4             | 81.0              |
+| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-pose.pt)       | 640                 | 67.6                  | 90.0               | 784.5                         | 2.59                               | 44.4             | 168.6             |
+| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt)       | 640                 | 69.2                  | 90.2               | 1607.1                        | 3.73                               | 69.4             | 263.2             |
+| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280                | 71.6                  | 91.2               | 4088.7                        | 10.04                              | 99.1             | 1066.4            |
+
+- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](https://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现
+
+</details>
+
+<details><summary>旋转检测 (DOTAv1)</summary>
+
+查看[旋转检测文档](https://docs.ultralytics.com/tasks/obb/)以获取这些在[DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/)上训练的模型的使用示例，其中包括15个预训练类别。
+
+| 模型                                                                                         | 尺寸<br><sup>(像素) | mAP<sup>test<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
+| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-obb.pt) | 1024                | 78.0               | 204.77                        | 3.57                               | 3.1              | 23.3              |
+| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-obb.pt) | 1024                | 79.5               | 424.88                        | 4.07                               | 11.4             | 76.3              |
+| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-obb.pt) | 1024                | 80.5               | 763.48                        | 7.61                               | 26.4             | 208.6             |
+| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-obb.pt) | 1024                | 80.7               | 1278.42                       | 11.83                              | 44.5             | 433.8             |
+| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-obb.pt) | 1024                | 81.36              | 1759.10                       | 13.23                              | 69.5             | 676.7             |
+
+- **mAP<sup>val</sup>** 值是基于单模型多尺度在 [DOTAv1](https://captain-whu.github.io/DOTA/index.html) 数据集上的结果。 <br>通过 `yolo val obb data=DOTAv1.yaml device=0 split=test` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu` 复现
+
+</details>
+
+<details><summary>分类 (ImageNet)</summary>
+
+查看[分类文档](https://docs.ultralytics.com/tasks/classify/)以获取这些在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/)上训练的模型的使用示例，其中包括1000个预训练类别。
+
+| 模型                                                                                         | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
+| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | ---------------- | ------------------------ |
+| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224                 | 69.0             | 88.3             | 12.9                          | 0.31                               | 2.7              | 4.3                      |
+| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224                 | 73.8             | 91.7             | 23.4                          | 0.35                               | 6.4              | 13.5                     |
+| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224                 | 76.8             | 93.5             | 85.4                          | 0.62                               | 17.0             | 42.7                     |
+| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224                 | 78.3             | 94.2             | 163.0                         | 0.87                               | 37.5             | 99.7                     |
+| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224                 | 79.0             | 94.6             | 232.0                         | 1.01                               | 57.4             | 154.8                    |
+
+- **acc** 值是模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。 <br>通过 `yolo val classify data=path/to/ImageNet device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 ImageNet val 图像进行平均计算的。 <br>通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现
+
+</details>
+
+## <div align="center">集成</div>
+
+我们与领先的AI平台的关键整合扩展了Ultralytics产品的功能，增强了数据集标签化、训练、可视化和模型管理等任务。探索Ultralytics如何与[Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic以及[OpenVINO](https://docs.ultralytics.com/integrations/openvino)合作，优化您的AI工作流程。
+
+<br>
+<a href="https://ultralytics.com/hub" 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://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://clear.ml/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://bit.ly/yolov8-readme-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://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
+</div>
+
+|                                                 Roboflow                                                  |                                  ClearML ⭐ NEW                                  |                                                     Comet ⭐ NEW                                                     |                                        Neural Magic ⭐ NEW                                        |
+| :-------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
+| 使用 [Roboflow](https://roboflow.com/?ref=ultralytics) 将您的自定义数据集直接标记并导出至 YOLOv8 进行训练 | 使用 [ClearML](https://clear.ml/)（开源！）自动跟踪、可视化，甚至远程训练 YOLOv8 | 免费且永久，[Comet](https://bit.ly/yolov8-readme-comet) 让您保存 YOLOv8 模型、恢复训练，并以交互式方式查看和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 使 YOLOv8 推理速度提高多达 6 倍 |
+
+## <div align="center">Ultralytics HUB</div>
+
+体验 [Ultralytics HUB](https://ultralytics.com/hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://ultralytics.com/app_install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！
+
+<a href="https://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>
+
+## <div align="center">贡献</div>
+
+我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing)以开始使用，并填写我们的[调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/ultralytics/graphs/contributors">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
+
+## <div align="center">许可证</div>
+
+Ultralytics 提供两种许可证选项以适应各种使用场景：
+
+- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件以了解更多细节。
+- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://ultralytics.com/license)与我们联系。
+
+## <div align="center">联系方式</div>
+
+对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)，并加入我们的 [Discord](https://ultralytics.com/discord) 社区进行问题和讨论！
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><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://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://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>

ultralytics/docker/Dockerfile

@@ -0,0 +1,89 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
+
+# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3
+FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime
+
+# Set environment variables
+# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library" https://github.com/pytorch/pytorch/issues/37377
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1 \
+    MKL_THREADING_LAYER=GNU
+
+# 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
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+# libsm6 required by libqxcb to create QT-based windows for visualization; set 'QT_DEBUG_PLUGINS=1' to test in docker
+RUN apt update \
+    && apt install --no-install-recommends -y gcc git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 libsm6
+
+# Security updates
+# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
+RUN apt upgrade --no-install-recommends -y openssl tar
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+# Pin TensorRT-cu12==10.1.0 to avoid 10.2.0 bug https://github.com/ultralytics/ultralytics/pull/14239 (note -cu12 must be used)
+RUN pip install -e ".[export]" "tensorrt-cu12==10.1.0" "albumentations>=1.4.6" comet pycocotools
+
+# Run exports to AutoInstall packages
+# Edge TPU export fails the first time so is run twice here
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32 || yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+RUN pip install "paddlepaddle>=2.6.0" x2paddle
+# Fix error: `np.bool` was a deprecated alias for the builtin `bool` segmentation error in Tests
+RUN pip install numpy==1.23.5
+# Remove exported models
+RUN rm -rf tmp
+
+
+# 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/docker/Dockerfile-arm64

@@ -0,0 +1,54 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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 (new)
+FROM arm64v8/debian:bookworm-slim
+
+# 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
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip unzip wget curl htop gcc libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install -e ".[export]"
+
+# Creates a symbolic link to make 'python' point to 'python3'
+RUN ln -sf /usr/bin/python3 /usr/bin/python
+
+
+# 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/docker/Dockerfile-conda

@@ -0,0 +1,46 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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
+RUN apt update \
+    && apt install --no-install-recommends -y libgl1
+
+# Copy contents
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Install conda packages
+# mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory'
+RUN conda config --set solver libmamba && \
+    conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia && \
+    conda install -c conda-forge ultralytics mkl
+    # conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=12.1 ultralytics mkl
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-cpu -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/docker/Dockerfile-cpu

@@ -0,0 +1,60 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM ubuntu:23.10
+
+# 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
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires Python<=3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+# RUN pip install "paddlepaddle>=2.6.0" x2paddle
+# Remove exported models
+RUN rm -rf tmp
+
+# Creates a symbolic link to make 'python' point to 'python3'
+RUN ln -sf /usr/bin/python3 /usr/bin/python
+
+
+# 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/docker/Dockerfile-jetson-jetpack4

@@ -0,0 +1,65 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack4.x for YOLOv8 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
+RUN apt update && \
+    apt install --no-install-recommends -y git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc
+
+# Create symbolic links for python3.8 and pip3
+RUN ln -sf /usr/bin/python3.8 /usr/bin/python3
+RUN 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
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Download onnxruntime-gpu 1.8.0 and tensorrt 8.2.0.6
+# Other versions can be seen in https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
+ADD https://nvidia.box.com/shared/static/gjqofg7rkg97z3gc8jeyup6t8n9j8xjw.whl onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl
+ADD https://forums.developer.nvidia.com/uploads/short-url/hASzFOm9YsJx6VVFrDW1g44CMmv.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install \
+    onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl \
+    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
+RUN pip install -e ".[export]"
+RUN rm *.whl
+
+# 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/docker/Dockerfile-jetson-jetpack5

@@ -0,0 +1,59 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson-jetson-jetpack5 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack5.x for YOLOv8 on Jetson Xavier NX, AGX Xavier, AGX Orin, Orin Nano and Orin NX
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch
+FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
+
+# 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
+# g++ required to build 'tflite_support' and 'lap' packages
+# libusb-1.0-0 required for 'tflite_support' package when exporting to TFLite
+# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
+RUN apt update \
+    && apt install --no-install-recommends -y gcc git zip unzip  wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Remove opencv-python from Ultralytics dependencies as it conflicts with opencv-python installed in base image
+RUN sed -i '/opencv-python/d' pyproject.toml
+
+# Download onnxruntime-gpu 1.15.1 for Jetson Linux 35.2.1 (JetPack 5.1). Other versions can be seen in https://elinux.org/Jetson_Zoo#ONNX_Runtime
+ADD https://nvidia.box.com/shared/static/mvdcltm9ewdy2d5nurkiqorofz1s53ww.whl onnxruntime_gpu-1.15.1-cp38-cp38-linux_aarch64.whl
+
+# Install pip packages manually for TensorRT compatibility https://github.com/NVIDIA/TensorRT/issues/2567
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install onnxruntime_gpu-1.15.1-cp38-cp38-linux_aarch64.whl
+RUN pip install -e ".[export]"
+RUN rm *.whl
+
+
+# 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/docker/Dockerfile-jetson-jetpack6

@@ -0,0 +1,55 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson-jetpack6 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack6.x for YOLOv8 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:r36.3.0
+
+# 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
+RUN apt update && \
+    apt install --no-install-recommends -y git python3-pip libopenmpi-dev libopenblas-base libomp-dev
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Download onnxruntime-gpu 1.18.0 from https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
+ADD https://nvidia.box.com/shared/static/48dtuob7meiw6ebgfsfqakc9vse62sg4.whl onnxruntime_gpu-1.18.0-cp310-cp310-linux_aarch64.whl
+
+# Pip install onnxruntime-gpu, torch, torchvision and ultralytics
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install \
+    onnxruntime_gpu-1.18.0-cp310-cp310-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.3.0-cp310-cp310-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.18.0a0+6043bc2-cp310-cp310-linux_aarch64.whl
+RUN pip install -e ".[export]"
+RUN rm *.whl
+
+# 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/docker/Dockerfile-python

@@ -0,0 +1,57 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
+
+# Use the official Python 3.10 slim-bookworm as base image
+FROM python:3.10-slim-bookworm
+
+# 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
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires Python<=3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+RUN pip install "paddlepaddle>=2.6.0" x2paddle
+# Remove exported models
+RUN rm -rf tmp
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-python && sudo docker build -f docker/Dockerfile-python -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-python && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# 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/docker/Dockerfile-runner

@@ -0,0 +1,45 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference tests
+
+# Start FROM Ultralytics GPU image
+FROM ultralytics/ultralytics:latest
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1 \
+    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
+RUN FILENAME=actions-runner-linux-x64-2.317.0.tar.gz && \
+    curl -o $FILENAME -L https://github.com/actions/runner/releases/download/v2.317.0/$FILENAME && \
+    tar xzf $FILENAME && \
+    rm $FILENAME
+
+# Install runner dependencies
+RUN pip install pytest-cov
+RUN ./bin/installdependencies.sh && \
+    apt-get -y install libicu-dev
+
+# Inline 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/docs/README.md

@@ -0,0 +1,146 @@
+<br>
+<a href="https://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
+
+[Ultralytics](https://ultralytics.com) Docs are the gateway to understanding and utilizing our cutting-edge machine learning tools. These documents are deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com) for your convenience.
+
+[![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)
+[![Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://ultralytics.com/discord)
+[![Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com)
+
+## 🛠️ 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://pepy.tech/project/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, ensure you have Git and Python 3 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 developer mode using pip (or pip3 for Python 3):
+
+    ```bash
+    pip install -e '.[dev]'
+    ```
+
+- This command installs the ultralytics package along with all development dependencies, allowing you to modify the package code and have the changes immediately reflected in your Python environment.
+
+## 🚀 Building and Serving Locally
+
+The `mkdocs serve` command builds and serves a local version of your MkDocs documentation, ideal for development and testing:
+
+```bash
+mkdocs serve
+```
+
+- #### Command Breakdown:
+
+    - `mkdocs` is the main MkDocs command-line interface.
+    - `serve` is the subcommand to build and locally serve your documentation.
+
+- 🧐 Note:
+
+    - Grasp changes to the docs in real-time as `mkdocs serve` supports live reloading.
+    - To stop the local server, press `CTRL+C`.
+
+## 🌍 Building and Serving Multi-Language
+
+Supporting multi-language documentation? Follow these steps:
+
+1. Stage all new language \*.md files with Git:
+
+    ```bash
+    git add docs/**/*.md -f
+    ```
+
+2. Build all languages to the `/site` folder, ensuring relevant root-level files are present:
+
+    ```bash
+    # Clear existing /site directory
+    rm -rf site
+
+    # Loop through each language config file and build
+    mkdocs build -f docs/mkdocs.yml
+    for file in docs/mkdocs_*.yml; do
+      echo "Building MkDocs site with $file"
+      mkdocs build -f "$file"
+    done
+    ```
+
+3. To preview your site, initiate a simple HTTP server:
+
+    ```bash
+    cd site
+    python -m http.server
+    # Open in your preferred browser
+    ```
+
+- 🖥️ Access the live site at `http://localhost:8000`.
+
+## 📤 Deploying Your Documentation Site
+
+Choose a hosting provider and deployment method for your MkDocs documentation:
+
+- Configure `mkdocs.yml` with deployment settings.
+- Use `mkdocs deploy` to build and deploy your site.
+
+* ### GitHub Pages Deployment Example:
+
+    ```bash
+    mkdocs gh-deploy
+    ```
+
+- Update the "Custom domain" in your repository's settings for a personalized URL.
+
+![196814117-fc16e711-d2be-4722-9536-b7c6d78fd167](https://user-images.githubusercontent.com/26833433/210150206-9e86dcd7-10af-43e4-9eb2-9518b3799eac.png)
+
+- For detailed deployment guidance, consult the [MkDocs documentation](https://www.mkdocs.org/user-guide/deploying-your-docs/).
+
+## 💡 Contribute
+
+We cherish the community's input as it drives Ultralytics open-source initiatives. Dive into the [Contributing Guide](https://docs.ultralytics.com/help/contributing) and share your thoughts via our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 to each contributor!
+
+![Ultralytics open-source contributors](https://github.com/ultralytics/assets/raw/main/im/image-contributors.png)
+
+## 📜 License
+
+Ultralytics Docs presents two licensing options:
+
+- **AGPL-3.0 License**: Perfect for academia and open collaboration. Details are in the [LICENSE](https://github.com/ultralytics/docs/blob/main/LICENSE) file.
+- **Enterprise License**: Tailored for commercial usage, offering a seamless blend of Ultralytics technology in your products. Learn more at [Ultralytics Licensing](https://ultralytics.com/license).
+
+## ✉️ Contact
+
+For bug reports and feature requests, navigate to [GitHub Issues](https://github.com/ultralytics/docs/issues). Engage with peers and the Ultralytics team on [Discord](https://ultralytics.com/discord) for enriching conversations!
+
+<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://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://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>

ultralytics/docs/build_docs.py

@@ -0,0 +1,258 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""
+Automates the building and post-processing of MkDocs documentation, particularly for projects with multilingual content.
+It streamlines the workflow for generating localized versions of the documentation and updating HTML links to ensure
+they are correctly formatted.
+
+Key Features:
+    - Automated building of MkDocs documentation: The script compiles both the main documentation and
+      any localized versions specified in separate MkDocs configuration files.
+    - Post-processing of generated HTML files: After the documentation is built, the script updates all
+      HTML files to remove the '.md' extension from internal links. This ensures that links in the built
+      HTML documentation correctly point to other HTML pages rather than Markdown files, which is crucial
+      for proper navigation within the web-based documentation.
+
+Usage:
+    - Run the script from the root directory of your MkDocs project.
+    - Ensure that MkDocs is installed and that all MkDocs configuration files (main and localized versions)
+      are present in the project directory.
+    - The script first builds the documentation using MkDocs, then scans the generated HTML files in the 'site'
+      directory to update the internal links.
+    - It's ideal for projects where the documentation is written in Markdown and needs to be served as a static website.
+
+Note:
+    - This script is built to be run in an environment where Python and MkDocs are installed and properly configured.
+"""
+
+import os
+import re
+import shutil
+import subprocess
+from pathlib import Path
+
+from bs4 import BeautifulSoup
+from 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"
+
+
+def prepare_docs_markdown(clone_repos=True):
+    """Build docs using mkdocs."""
+    if SITE.exists():
+        print(f"Removing existing {SITE}")
+        shutil.rmtree(SITE)
+
+    # Get hub-sdk repo
+    if clone_repos:
+        repo = "https://github.com/ultralytics/hub-sdk"
+        local_dir = DOCS.parent / Path(repo).name
+        if not local_dir.exists():
+            os.system(f"git clone {repo} {local_dir}")
+        os.system(f"git -C {local_dir} pull")  # update repo
+        shutil.rmtree(DOCS / "en/hub/sdk", ignore_errors=True)  # delete if exists
+        shutil.copytree(local_dir / "docs", DOCS / "en/hub/sdk")  # for docs
+        shutil.rmtree(DOCS.parent / "hub_sdk", ignore_errors=True)  # delete if exists
+        shutil.copytree(local_dir / "hub_sdk", DOCS.parent / "hub_sdk")  # for mkdocstrings
+        print(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_page_title(file_path: Path, new_title: str):
+    """Update the title of an HTML file."""
+    # Read the content of the file
+    with open(file_path, encoding="utf-8") as file:
+        content = file.read()
+
+    # Replace the existing title with the new title
+    updated_content = re.sub(r"<title>.*?</title>", f"<title>{new_title}</title>", content)
+
+    # Write the updated content back to the file
+    with open(file_path, "w", encoding="utf-8") as file:
+        file.write(updated_content)
+
+
+def update_html_head(script=""):
+    """Update the HTML head section of each file."""
+    html_files = Path(SITE).rglob("*.html")
+    for html_file in tqdm(html_files, desc="Processing HTML files"):
+        with html_file.open("r", encoding="utf-8") as file:
+            html_content = file.read()
+
+        if script in html_content:  # script already in HTML file
+            return
+
+        head_end_index = html_content.lower().rfind("</head>")
+        if head_end_index != -1:
+            # Add the specified JavaScript to the HTML file just before the end of the head tag.
+            new_html_content = html_content[:head_end_index] + script + html_content[head_end_index:]
+            with html_file.open("w", encoding="utf-8") as file:
+                file.write(new_html_content)
+
+
+def update_subdir_edit_links(subdir="", docs_url=""):
+    """Update the HTML head section of each file."""
+    if str(subdir[0]) == "/":
+        subdir = str(subdir[0])[1:]
+    html_files = (SITE / subdir).rglob("*.html")
+    for html_file in tqdm(html_files, desc="Processing subdir files"):
+        with html_file.open("r", encoding="utf-8") as file:
+            soup = BeautifulSoup(file, "html.parser")
+
+        # Find the anchor tag and update its href attribute
+        a_tag = soup.find("a", {"class": "md-content__button md-icon"})
+        if a_tag and a_tag["title"] == "Edit this page":
+            a_tag["href"] = f"{docs_url}{a_tag['href'].split(subdir)[-1]}"
+
+        # Write the updated HTML back to the file
+        with open(html_file, "w", encoding="utf-8") as file:
+            file.write(str(soup))
+
+
+def update_markdown_files(md_filepath: Path):
+    """Creates or updates 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") and "macros" not in md_filepath.parts:  # skip macros directory
+            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():
+    """Updates titles, edit links, head sections, and converts plaintext links in HTML documentation."""
+    # Update 404 titles
+    update_page_title(SITE / "404.html", new_title="Ultralytics Docs - Not Found")
+
+    # Update edit links
+    update_subdir_edit_links(
+        subdir="hub/sdk/",  # do not use leading slash
+        docs_url="https://github.com/ultralytics/hub-sdk/tree/main/docs/",
+    )
+
+    # Convert plaintext links to HTML hyperlinks
+    files_modified = 0
+    for html_file in tqdm(SITE.rglob("*.html"), desc="Converting plaintext links"):
+        with open(html_file, "r", encoding="utf-8") as file:
+            content = file.read()
+        updated_content = convert_plaintext_links_to_html(content)
+        if updated_content != content:
+            with open(html_file, "w", encoding="utf-8") as file:
+                file.write(updated_content)
+            files_modified += 1
+    print(f"Modified plaintext links in {files_modified} files.")
+
+    # Update HTML file head section
+    script = ""
+    if any(script):
+        update_html_head(script)
+
+    # Delete the /macros directory from the built site
+    macros_dir = SITE / "macros"
+    if macros_dir.exists():
+        print(f"Removing /macros directory from site: {macros_dir}")
+        shutil.rmtree(macros_dir)
+
+
+def convert_plaintext_links_to_html(content):
+    """Convert plaintext links to HTML hyperlinks in the main content area only."""
+    soup = BeautifulSoup(content, "html.parser")
+
+    # Find the main content area (adjust this selector based on your HTML structure)
+    main_content = soup.find("main") or soup.find("div", class_="md-content")
+    if not main_content:
+        return content  # Return original content if main content area not found
+
+    modified = False
+    for paragraph in main_content.find_all(["p", "li"]):  # Focus on paragraphs and list items
+        for text_node in paragraph.find_all(string=True, recursive=False):
+            if text_node.parent.name not in {"a", "code"}:  # Ignore links and code blocks
+                new_text = re.sub(
+                    r'(https?://[^\s()<>]+(?:\.[^\s()<>]+)+)(?<![.,:;\'"])',
+                    r'<a href="\1">\1</a>',
+                    str(text_node),
+                )
+                if "<a" in new_text:
+                    new_soup = BeautifulSoup(new_text, "html.parser")
+                    text_node.replace_with(new_soup)
+                    modified = True
+
+    return str(soup) if modified else content
+
+
+def remove_macros():
+    """Removes the /macros directory and related entries in sitemap.xml from the built site."""
+    shutil.rmtree(SITE / "macros", ignore_errors=True)
+    (SITE / "sitemap.xml.gz").unlink(missing_ok=True)
+
+    # Process sitemap.xml
+    sitemap = SITE / "sitemap.xml"
+    lines = sitemap.read_text(encoding="utf-8").splitlines(keepends=True)
+
+    # Find indices of '/macros/' lines
+    macros_indices = [i for i, line in enumerate(lines) if "/macros/" in line]
+
+    # Create a set of indices to remove (including lines before and after)
+    indices_to_remove = set()
+    for i in macros_indices:
+        indices_to_remove.update(range(i - 1, i + 4))  # i-1, i, i+1, i+2, i+3
+
+    # Create new list of lines, excluding the ones to remove
+    new_lines = [line for i, line in enumerate(lines) if i not in indices_to_remove]
+
+    # Write the cleaned content back to the file
+    sitemap.write_text("".join(new_lines), encoding="utf-8")
+
+    print(f"Removed {len(macros_indices)} URLs containing '/macros/' from {sitemap}")
+
+
+def main():
+    """Builds docs, updates titles and edit links, and prints local server command."""
+    prepare_docs_markdown()
+
+    # Build the main documentation
+    print(f"Building docs from {DOCS}")
+    subprocess.run(f"mkdocs build -f {DOCS.parent}/mkdocs.yml --strict", check=True, shell=True)
+    remove_macros()
+    print(f"Site built at {SITE}")
+
+    # Update docs HTML pages
+    update_docs_html()
+
+    # Show command to serve built website
+    print('Docs built correctly ✅\nServe site at http://localhost:8000 with "python -m http.server --directory site"')
+
+
+if __name__ == "__main__":
+    main()

ultralytics/docs/build_reference.py

@@ -0,0 +1,147 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""
+Helper file to build Ultralytics Docs reference section. Recursively walks through ultralytics dir and builds an MkDocs
+reference section of *.md files composed of classes and functions, and also creates a nav menu for use in mkdocs.yaml.
+
+Note: Must be run from repository root directory. Do not run from docs directory.
+"""
+
+import re
+import subprocess
+from collections import defaultdict
+from pathlib import Path
+
+# Constants
+hub_sdk = False
+if hub_sdk:
+    PACKAGE_DIR = Path("/Users/glennjocher/PycharmProjects/hub-sdk/hub_sdk")
+    REFERENCE_DIR = PACKAGE_DIR.parent / "docs/reference"
+    GITHUB_REPO = "ultralytics/hub-sdk"
+else:
+    FILE = Path(__file__).resolve()
+    PACKAGE_DIR = FILE.parents[1] / "ultralytics"  # i.e. /Users/glennjocher/PycharmProjects/ultralytics/ultralytics
+    REFERENCE_DIR = PACKAGE_DIR.parent / "docs/en/reference"
+    GITHUB_REPO = "ultralytics/ultralytics"
+
+
+def extract_classes_and_functions(filepath: Path) -> tuple:
+    """Extracts class and function names from a given Python file."""
+    content = filepath.read_text()
+    class_pattern = r"(?:^|\n)class\s(\w+)(?:\(|:)"
+    func_pattern = r"(?:^|\n)def\s(\w+)\("
+
+    classes = re.findall(class_pattern, content)
+    functions = re.findall(func_pattern, content)
+
+    return classes, functions
+
+
+def create_markdown(py_filepath: Path, module_path: str, classes: list, functions: list):
+    """Creates a Markdown file containing the API reference for the given Python module."""
+    md_filepath = py_filepath.with_suffix(".md")
+    exists = md_filepath.exists()
+
+    # Read existing content and keep header content between first two ---
+    header_content = ""
+    if exists:
+        existing_content = md_filepath.read_text()
+        header_parts = existing_content.split("---")
+        for part in header_parts:
+            if "description:" in part or "comments:" in part:
+                header_content += f"---{part}---\n\n"
+    if not any(header_content):
+        header_content = "---\ndescription: TODO ADD DESCRIPTION\nkeywords: TODO ADD KEYWORDS\n---\n\n"
+
+    module_name = module_path.replace(".__init__", "")
+    module_path = module_path.replace(".", "/")
+    url = f"https://github.com/{GITHUB_REPO}/blob/main/{module_path}.py"
+    edit = f"https://github.com/{GITHUB_REPO}/edit/main/{module_path}.py"
+    pretty = url.replace("__init__.py", "\\_\\_init\\_\\_.py")  # properly display __init__.py filenames
+    title_content = (
+        f"# Reference for `{module_path}.py`\n\n"
+        f"!!! Note\n\n"
+        f"    This file is available at [{pretty}]({url}). If you spot a problem please help fix it by [contributing]"
+        f"(https://docs.ultralytics.com/help/contributing/) a [Pull Request]({edit}) 🛠️. Thank you 🙏!\n\n"
+    )
+    md_content = ["<br>\n"] + [f"## ::: {module_name}.{class_name}\n\n<br><br><hr><br>\n" for class_name in classes]
+    md_content.extend(f"## ::: {module_name}.{func_name}\n\n<br><br><hr><br>\n" for func_name in functions)
+    md_content[-1] = md_content[-1].replace("<hr><br>", "")  # remove last horizontal line
+    md_content = header_content + title_content + "\n".join(md_content)
+    if not md_content.endswith("\n"):
+        md_content += "\n"
+
+    md_filepath.parent.mkdir(parents=True, exist_ok=True)
+    md_filepath.write_text(md_content)
+
+    if not exists:
+        # Add new markdown file to the git staging area
+        print(f"Created new file '{md_filepath}'")
+        subprocess.run(["git", "add", "-f", str(md_filepath)], check=True, cwd=PACKAGE_DIR)
+
+    return md_filepath.relative_to(PACKAGE_DIR.parent)
+
+
+def nested_dict() -> defaultdict:
+    """Creates and returns a nested defaultdict."""
+    return defaultdict(nested_dict)
+
+
+def sort_nested_dict(d: dict) -> dict:
+    """Sorts a nested dictionary recursively."""
+    return {key: sort_nested_dict(value) if isinstance(value, dict) else value for key, value in sorted(d.items())}
+
+
+def create_nav_menu_yaml(nav_items: list, save: bool = False):
+    """Creates a YAML file for the navigation menu based on the provided list of items."""
+    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 the first two parts (docs and reference) and the last part (filename)
+            current_level = current_level[part]
+
+        md_file_name = parts[-1].replace(".md", "")
+        current_level[md_file_name] = item
+
+    nav_tree_sorted = sort_nested_dict(nav_tree)
+
+    def _dict_to_yaml(d, level=0):
+        """Converts a nested dictionary to a YAML-formatted string with indentation."""
+        yaml_str = ""
+        indent = "  " * level
+        for k, v in d.items():
+            if isinstance(v, dict):
+                yaml_str += f"{indent}- {k}:\n{_dict_to_yaml(v, level + 1)}"
+            else:
+                yaml_str += f"{indent}- {k}: {str(v).replace('docs/en/', '')}\n"
+        return yaml_str
+
+    # Print updated YAML reference section
+    print("Scan complete, new mkdocs.yaml reference section is:\n\n", _dict_to_yaml(nav_tree_sorted))
+
+    # Save new YAML reference section
+    if save:
+        (PACKAGE_DIR.parent / "nav_menu_updated.yml").write_text(_dict_to_yaml(nav_tree_sorted))
+
+
+def main():
+    """Main function to extract class and function names, create Markdown files, and generate a YAML navigation menu."""
+    nav_items = []
+
+    for py_filepath in PACKAGE_DIR.rglob("*.py"):
+        classes, functions = extract_classes_and_functions(py_filepath)
+
+        if classes or functions:
+            py_filepath_rel = py_filepath.relative_to(PACKAGE_DIR)
+            md_filepath = REFERENCE_DIR / py_filepath_rel
+            module_path = f"{PACKAGE_DIR.name}.{py_filepath_rel.with_suffix('').as_posix().replace('/', '.')}"
+            md_rel_filepath = create_markdown(md_filepath, module_path, classes, functions)
+            nav_items.append(str(md_rel_filepath))
+
+    create_nav_menu_yaml(nav_items)
+
+
+if __name__ == "__main__":
+    main()

ultralytics/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://ultralytics.com) "Under Construction" page! Here, we're hard at work developing the next generation of AI and 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 and services that will transform your AI and ML experience.
+- **New Horizons:** Anticipate novel products that redefine AI and ML capabilities.
+- **Enhanced Services:** We're upgrading our services for greater efficiency and user-friendliness.
+
+## Stay Updated 🚧
+
+This placeholder page is your first stop for upcoming developments. Keep an eye out for:
+
+- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news.
+- **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers.
+- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights.
+
+## We Value Your Input 🗣️
+
+Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/survey).
+
+## Thank You, Community! 🌍
+
+Your [contributions](https://docs.ultralytics.com/help/contributing) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
+
+---
+
+Excited for what's coming? Bookmark this page and get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖

ultralytics/docs/en/CNAME

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

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

@@ -0,0 +1,149 @@
+---
+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.
+
+## 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 models in object recognition tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (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 and computer vision.
+
+## Usage
+
+To train a YOLO model on the Caltech-101 dataset for 100 epochs, 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("yolov8n-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=yolov8n-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 object recognition tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239366386-44171121-b745-4206-9b59-a3be41e16089.png)
+
+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 (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("yolov8n-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=yolov8n-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 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.