├── .github ├── ISSUE_TEMPLATE │ ├── bug-report.yml │ ├── config.yml │ ├── feature-request.yml │ └── question.yml ├── dependabot.yml └── workflows │ ├── ci.yml │ ├── cla.yml │ ├── format.yml │ ├── links.yml │ └── stale.yml ├── .gitignore ├── LICENSE ├── README.md ├── README.zh-CN.md ├── example_datasets ├── coco8-human.zip ├── coco8-human │ ├── LICENSE │ ├── README.md │ ├── coco8-human.yaml │ ├── images │ │ ├── train │ │ │ ├── 000000000113.jpg │ │ │ ├── 000000000294.jpg │ │ │ ├── 000000000326.jpg │ │ │ └── 000000542449.jpg │ │ └── val │ │ │ ├── 000000000241.jpg │ │ │ ├── 000000000589.jpg │ │ │ ├── 000000000897.jpg │ │ │ └── 000000001592.jpg │ └── labels │ │ ├── train │ │ ├── 000000000113.txt │ │ ├── 000000000294.txt │ │ ├── 000000000326.txt │ │ └── 000000542449.txt │ │ └── val │ │ ├── 000000000241.txt │ │ ├── 000000000589.txt │ │ ├── 000000000897.txt │ │ └── 000000001592.txt ├── coco8-pose.zip ├── coco8-pose │ ├── LICENSE │ ├── README.md │ ├── coco8-pose.yaml │ ├── images │ │ ├── train │ │ │ ├── 000000000036.jpg │ │ │ ├── 000000000049.jpg │ │ │ ├── 000000000077.jpg │ │ │ └── 000000000086.jpg │ │ └── val │ │ │ ├── 000000000110.jpg │ │ │ ├── 000000000113.jpg │ │ │ ├── 000000000136.jpg │ │ │ └── 000000000151.jpg │ └── labels │ │ ├── train │ │ ├── 000000000036.txt │ │ ├── 000000000049.txt │ │ ├── 000000000077.txt │ │ └── 000000000086.txt │ │ └── val │ │ ├── 000000000110.txt │ │ ├── 000000000113.txt │ │ ├── 000000000136.txt │ │ └── 000000000151.txt ├── coco8-seg.zip ├── coco8-seg │ ├── LICENSE │ ├── README.md │ ├── coco8-seg.yaml │ ├── images │ │ ├── train │ │ │ ├── 000000000009.jpg │ │ │ ├── 000000000025.jpg │ │ │ ├── 000000000030.jpg │ │ │ └── 000000000034.jpg │ │ └── val │ │ │ ├── 000000000036.jpg │ │ │ ├── 000000000042.jpg │ │ │ ├── 000000000049.jpg │ │ │ └── 000000000061.jpg │ └── labels │ │ ├── train │ │ ├── 000000000009.txt │ │ ├── 000000000025.txt │ │ ├── 000000000030.txt │ │ └── 000000000034.txt │ │ └── val │ │ ├── 000000000036.txt │ │ ├── 000000000042.txt │ │ ├── 000000000049.txt │ │ └── 000000000061.txt ├── coco8.zip ├── coco8 │ ├── LICENSE │ ├── README.md │ ├── coco8.yaml │ ├── images │ │ ├── train │ │ │ ├── 000000000009.jpg │ │ │ ├── 000000000025.jpg │ │ │ ├── 000000000030.jpg │ │ │ └── 000000000034.jpg │ │ └── val │ │ │ ├── 000000000036.jpg │ │ │ ├── 000000000042.jpg │ │ │ ├── 000000000049.jpg │ │ │ └── 000000000061.jpg │ └── labels │ │ ├── train │ │ ├── 000000000009.txt │ │ ├── 000000000025.txt │ │ ├── 000000000030.txt │ │ └── 000000000034.txt │ │ └── val │ │ ├── 000000000036.txt │ │ ├── 000000000042.txt │ │ ├── 000000000049.txt │ │ └── 000000000061.txt ├── dota8.zip ├── dota8 │ ├── LICENSE │ ├── README.md │ ├── dota8.yaml │ ├── images │ │ ├── train │ │ │ ├── P0861__1024__0___1648.jpg │ │ │ ├── P1053__1024__0___90.jpg │ │ │ ├── P1142__1024__0___824.jpg │ │ │ └── P1161__1024__3296___1648.jpg │ │ └── val │ │ │ ├── P1470__1024__3296___1648.jpg │ │ │ ├── P1571__1024__2976___0.jpg │ │ │ ├── P1580__1024__824___824.jpg │ │ │ └── P1724__1024__0___824.jpg │ └── labels │ │ ├── train.cache │ │ ├── train │ │ ├── P0861__1024__0___1648.txt │ │ ├── P1053__1024__0___90.txt │ │ ├── P1142__1024__0___824.txt │ │ └── P1161__1024__3296___1648.txt │ │ ├── val.cache │ │ └── val │ │ ├── P1470__1024__3296___1648.txt │ │ ├── P1571__1024__2976___0.txt │ │ ├── P1580__1024__824___824.txt │ │ └── P1724__1024__0___824.txt ├── imagenet10.zip └── imagenet10 │ ├── train │ ├── cock │ │ └── n01514668_23019.JPEG │ ├── electric_ray │ │ └── n01496331_33488.JPEG │ ├── goldfish │ │ └── n01443537_22323.JPEG │ ├── great_white_shark │ │ └── n01484850_7967.JPEG │ ├── hammerhead │ │ └── n01494475_16184.JPEG │ ├── hen │ │ └── n01514859_3376.JPEG │ ├── ostrich │ │ └── n01518878_42327.JPEG │ ├── stingray │ │ └── n01498041_4566.JPEG │ ├── tench │ │ ├── n01440764_2923 copy.JPEG │ │ └── n01440764_2923.JPEG │ └── tiger_shark │ │ └── n01491361_2692.JPEG │ └── val │ ├── cock │ └── ILSVRC2012_val_00003606.JPEG │ ├── electric_ray │ └── ILSVRC2012_val_00042751.JPEG │ ├── goldfish │ ├── ILSVRC2012_val_00002241 copy.JPEG │ └── ILSVRC2012_val_00002241.JPEG │ ├── great_white_shark │ └── ILSVRC2012_val_00044052.JPEG │ ├── hammerhead │ └── ILSVRC2012_val_00021325.JPEG │ ├── hen │ └── ILSVRC2012_val_00021430.JPEG │ ├── ostrich │ └── ILSVRC2012_val_00017839.JPEG │ ├── stingray │ └── ILSVRC2012_val_00006402.JPEG │ ├── tench │ └── ILSVRC2012_val_00041939.JPEG │ └── tiger_shark │ └── ILSVRC2012_val_00030926.JPEG ├── hub.ipynb └── requirements.txt /.github/ISSUE_TEMPLATE/bug-report.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | name: 🐛 Bug Report 4 | # title: " " 5 | description: Problems with HUB 6 | labels: [bug, triage] 7 | body: 8 | - type: markdown 9 | attributes: 10 | value: | 11 | Thank you for submitting an [Ultralytics HUB](https://hub.ultralytics.com) 🐛 Bug Report! 12 | 13 | - type: checkboxes 14 | attributes: 15 | label: Search before asking 16 | description: > 17 | Please search the [issues](https://github.com/ultralytics/hub/issues) to see if a similar bug report already exists. 18 | options: 19 | - label: > 20 | I have searched the HUB [issues](https://github.com/ultralytics/hub/issues) and found no similar bug report. 21 | required: true 22 | 23 | - type: dropdown 24 | attributes: 25 | label: HUB Component 26 | description: | 27 | Please select the part of the HUB where you found the bug. 28 | multiple: true 29 | options: 30 | - "Signin" 31 | - "Datasets" 32 | - "Models" 33 | - "Projects" 34 | - "Training" 35 | - "Export" 36 | - "Inference" 37 | - "Settings" 38 | - "API Key" 39 | 40 | validations: 41 | required: false 42 | 43 | - type: textarea 44 | attributes: 45 | label: Bug 46 | description: Provide console output with error messages and/or screenshots of the bug. 47 | placeholder: | 48 | 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. 49 | validations: 50 | required: true 51 | 52 | - type: textarea 53 | attributes: 54 | label: Environment 55 | description: Please specify the software and hardware you used to produce the bug. 56 | placeholder: | 57 | - Computer: iMac (Retina 5K, 27-inch, Late 2020) 58 | - OS: MacOS 11.6.1 59 | - Browser: Chrome Version 95.0.4638.69 (Official Build) (x86_64) 60 | 61 | validations: 62 | required: false 63 | 64 | - type: textarea 65 | attributes: 66 | label: Minimal Reproducible Example 67 | description: > 68 | 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. 69 | This is referred to by community members as creating a [minimal reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). 70 | placeholder: | 71 | Steps to reproduce the problem, i.e.: 72 | 1. Login to Hub 73 | 2. Click Datasets 74 | 3. ... 75 | validations: 76 | required: false 77 | 78 | - type: textarea 79 | attributes: 80 | label: Additional 81 | description: Anything else you would like to share? 82 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/config.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | blank_issues_enabled: true 4 | contact_links: 5 | - name: 📄 Docs 6 | url: https://docs.ultralytics.com/ 7 | about: Full Ultralytics Documentation 8 | - name: 💬 Forum 9 | url: https://community.ultralytics.com/ 10 | about: Ask on Ultralytics Community Forum 11 | - name: 🎧 Discord 12 | url: https://ultralytics.com/discord 13 | about: Ask on Ultralytics Discord 14 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature-request.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | name: 🚀 Feature Request 4 | description: Suggest a HUB idea 5 | # title: " " 6 | labels: [enhancement] 7 | body: 8 | - type: markdown 9 | attributes: 10 | value: | 11 | Thank you for submitting an [Ultralytics HUB](https://hub.ultralytics.com) 🚀 Feature Request! 12 | 13 | - type: checkboxes 14 | attributes: 15 | label: Search before asking 16 | description: > 17 | Please search the [issues](https://github.com/ultralytics/hub/issues) to see if a similar feature request already exists. 18 | options: 19 | - label: > 20 | I have searched the HUB [issues](https://github.com/ultralytics/hub/issues) and found no similar feature requests. 21 | required: true 22 | 23 | - type: textarea 24 | attributes: 25 | label: Description 26 | description: A short description of your feature. 27 | placeholder: | 28 | What new feature would you like to see in HUB? 29 | validations: 30 | required: true 31 | 32 | - type: textarea 33 | attributes: 34 | label: Use case 35 | description: | 36 | Describe the use case of your feature request. It will help us understand and prioritize the feature request. 37 | placeholder: | 38 | How would this feature be used, and who would use it? 39 | 40 | - type: textarea 41 | attributes: 42 | label: Additional 43 | description: Anything else you would like to share? 44 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/question.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | name: ❓ Question 4 | description: Ask a HUB question 5 | # title: " " 6 | labels: [question] 7 | body: 8 | - type: markdown 9 | attributes: 10 | value: | 11 | Thank you for asking an [Ultralytics HUB](https://hub.ultralytics.com) ❓ Question! 12 | 13 | - type: checkboxes 14 | attributes: 15 | label: Search before asking 16 | description: > 17 | Please search the [issues](https://github.com/ultralytics/hub/issues) and [discussions](https://github.com/ultralytics/hub/discussions) to see if a similar question already exists. 18 | options: 19 | - label: > 20 | I have searched the HUB [issues](https://github.com/ultralytics/hub/issues) and [discussions](https://github.com/ultralytics/hub/discussions) and found no similar questions. 21 | required: true 22 | 23 | - type: textarea 24 | attributes: 25 | label: Question 26 | description: What is your question? 27 | placeholder: | 28 | 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. 29 | validations: 30 | required: true 31 | 32 | - type: textarea 33 | attributes: 34 | label: Additional 35 | description: Anything else you would like to share? 36 | -------------------------------------------------------------------------------- /.github/dependabot.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # Dependabot for package version updates 4 | # https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates 5 | 6 | version: 2 7 | updates: 8 | - package-ecosystem: pip 9 | directory: "/" 10 | schedule: 11 | interval: weekly 12 | time: "04:00" 13 | open-pull-requests-limit: 10 14 | reviewers: 15 | - glenn-jocher 16 | labels: 17 | - dependencies 18 | 19 | - package-ecosystem: github-actions 20 | directory: "/.github/workflows" 21 | schedule: 22 | interval: weekly 23 | time: "04:00" 24 | open-pull-requests-limit: 5 25 | reviewers: 26 | - glenn-jocher 27 | labels: 28 | - dependencies 29 | -------------------------------------------------------------------------------- /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | name: HUB CI 4 | 5 | on: 6 | push: 7 | branches: [main] 8 | pull_request: 9 | branches: [main] 10 | workflow_dispatch: 11 | schedule: 12 | - cron: "0 2 * * *" # runs every 24 hours, starting at 02:00 UTC 13 | 14 | env: 15 | API_KEY: ${{ secrets.ULTRALYTICS_HUB_API_KEY }} 16 | MODEL_ID: ${{ secrets.ULTRALYTICS_HUB_MODEL_ID }} 17 | HUB_TEAM_SLACK_ID: ${{ secrets.HUB_TEAM_SLACK_ID }} 18 | 19 | jobs: 20 | Tests: 21 | runs-on: ${{ matrix.os }} 22 | strategy: 23 | fail-fast: false 24 | matrix: 25 | os: [ubuntu-latest] 26 | python-version: ["3.11"] 27 | timeout-minutes: 60 28 | steps: 29 | - uses: actions/checkout@v4 30 | - uses: actions/setup-python@v5 31 | with: 32 | python-version: ${{ matrix.python-version }} 33 | - uses: astral-sh/setup-uv@v6 34 | with: 35 | enable-cache: false 36 | - name: Install requirements 37 | run: | 38 | uv pip install --system ultralytics hub-sdk --extra-index-url https://download.pytorch.org/whl/cpu 39 | - name: Check environment 40 | run: | 41 | yolo checks 42 | uv pip list 43 | - name: Test HUB training 44 | continue-on-error: false 45 | shell: python 46 | run: | 47 | import os 48 | from ultralytics import YOLO, hub 49 | api_key, model_id = os.environ['API_KEY'], os.environ['MODEL_ID'] 50 | hub.login(api_key) 51 | hub.reset_model(model_id) 52 | model = YOLO('https://hub.ultralytics.com/models/' + model_id) 53 | model.train() 54 | - name: Test HUB inference API 55 | shell: python 56 | run: | 57 | import os 58 | import requests 59 | import json 60 | from io import BytesIO 61 | api_key, model_id = os.environ['API_KEY'], os.environ['MODEL_ID'] 62 | url = f"https://api.ultralytics.com/v1/predict/{model_id}" 63 | headers = {"x-api-key": api_key} 64 | data = {"size": 320, "confidence": 0.25, "iou": 0.45} 65 | f = BytesIO(requests.get("https://github.com/ultralytics/assets/releases/download/v0.0.0/zidane.jpg").content) 66 | response = requests.post(url, headers=headers, data=data, files={"image": f}) 67 | response.raise_for_status() # ensure we got a successful response 68 | print(json.dumps(response.json(), indent=2)) 69 | - name: Test HUB export 70 | if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' 71 | shell: python 72 | run: | 73 | import os 74 | import time 75 | from ultralytics import YOLO, checks, hub 76 | checks() 77 | api_key, model_id = os.environ['API_KEY'], os.environ['MODEL_ID'] 78 | hub.login(api_key) 79 | success = [] 80 | for f in ('torchscript', 'onnx', 'openvino', 'coreml', 'saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs', 81 | 'paddle', 'ultralytics_tflite', 'ultralytics_coreml', 'ncnn'): 82 | backoff_times = [60, 120, 240, 480] # exponential backoff waits in seconds 83 | for wait_time in backoff_times: 84 | try: 85 | print(f'Starting {f} export... ', end='') 86 | hub.export_model(model_id, format=f) 87 | time.sleep(wait_time) # wait for export to complete 88 | success.append(hub.get_export(model_id, format=f)['success']) 89 | print('done ✅' if success[-1] else 'Error ❌') 90 | break # exit the loop if no exception is raised 91 | except Exception as e: 92 | if wait_time == backoff_times[-1]: # if it's the last backoff time 93 | success.append(False) 94 | print(f'Error ❌ {f} export {e}') 95 | else: 96 | print(f'Retrying {f} export in {wait_time // 60} minutes due to error: {e}') 97 | time.sleep(wait_time) 98 | assert all(success) 99 | - name: Notify on failure 100 | if: (cancelled() || failure()) && github.repository == 'ultralytics/hub' && (github.event_name == 'schedule' || github.event_name == 'push') && github.run_attempt == '1' 101 | uses: slackapi/slack-github-action@v2.1.0 102 | with: 103 | webhook-type: incoming-webhook 104 | webhook: ${{ secrets.SLACK_WEBHOOK_URL_HUBWEB }} 105 | payload: | 106 | text: " 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" 107 | -------------------------------------------------------------------------------- /.github/workflows/cla.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA 4 | # This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged 5 | 6 | name: CLA Assistant 7 | on: 8 | issue_comment: 9 | types: 10 | - created 11 | pull_request_target: 12 | types: 13 | - reopened 14 | - opened 15 | - synchronize 16 | 17 | permissions: 18 | actions: write 19 | contents: write 20 | pull-requests: write 21 | statuses: write 22 | 23 | jobs: 24 | CLA: 25 | if: github.repository == 'ultralytics/hub' 26 | runs-on: ubuntu-latest 27 | steps: 28 | - name: CLA Assistant 29 | 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' 30 | uses: contributor-assistant/github-action@v2.6.1 31 | env: 32 | GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} 33 | # Must be repository secret PAT 34 | PERSONAL_ACCESS_TOKEN: ${{ secrets._GITHUB_TOKEN }} 35 | with: 36 | path-to-signatures: "signatures/version1/cla.json" 37 | path-to-document: "https://docs.ultralytics.com/help/CLA" # CLA document 38 | # Branch must not be protected 39 | branch: cla-signatures 40 | allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot* 41 | 42 | remote-organization-name: ultralytics 43 | remote-repository-name: cla 44 | custom-pr-sign-comment: "I have read the CLA Document and I sign the CLA" 45 | custom-allsigned-prcomment: All Contributors have signed the CLA. ✅ 46 | -------------------------------------------------------------------------------- /.github/workflows/format.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # Ultralytics Actions https://github.com/ultralytics/actions 4 | # This workflow formats code and documentation in PRs to Ultralytics standards 5 | 6 | name: Ultralytics Actions 7 | 8 | on: 9 | issues: 10 | types: [opened] 11 | pull_request: 12 | branches: [main] 13 | types: [opened, closed, synchronize, review_requested] 14 | 15 | permissions: 16 | contents: write # Modify code in PRs 17 | pull-requests: write # Add comments and labels to PRs 18 | issues: write # Add comments and labels to issues 19 | 20 | jobs: 21 | actions: 22 | runs-on: ubuntu-latest 23 | steps: 24 | - name: Run Ultralytics Actions 25 | uses: ultralytics/actions@main 26 | with: 27 | token: ${{ secrets._GITHUB_TOKEN || secrets.GITHUB_TOKEN }} # Auto-generated token 28 | labels: true # Auto-label issues/PRs using AI 29 | python: true # Format Python with Ruff and docformatter 30 | prettier: true # Format YAML, JSON, Markdown, CSS 31 | spelling: true # Check spelling with codespell 32 | links: false # Check broken links with Lychee 33 | summary: true # Generate AI-powered PR summaries 34 | openai_api_key: ${{ secrets.OPENAI_API_KEY }} # Powers PR summaries, labels and comments 35 | brave_api_key: ${{ secrets.BRAVE_API_KEY }} # Used for broken link resolution 36 | first_issue_response: | 37 | 👋 Hello @${{ github.actor }}, thank you for raising an issue about [Ultralytics HUB](https://hub.ultralytics.com) 🚀! Please visit our [HUB Docs](https://docs.ultralytics.com/hub) to learn more: 38 | - [**Quickstart**](https://docs.ultralytics.com/hub/quickstart). Start training and deploying YOLO models with HUB in seconds. 39 | - [**Datasets: Preparing and Uploading**](https://docs.ultralytics.com/hub/datasets). Learn how to prepare and upload your datasets to HUB in YOLO format. 40 | - [**Projects: Creating and Managing**](https://docs.ultralytics.com/hub/projects). Group your models into projects for improved organization. 41 | - [**Models: Training and Exporting**](https://docs.ultralytics.com/hub/models). Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment. 42 | - [**Integrations**](https://docs.ultralytics.com/hub/integrations). Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle. 43 | - [**Ultralytics HUB App**](https://docs.ultralytics.com/hub/app). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device. 44 | * [**iOS**](https://docs.ultralytics.com/hub/app/ios). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads. 45 | * [**Android**](https://docs.ultralytics.com/hub/app/android). Explore TFLite acceleration on mobile devices. 46 | - [**Inference API**](https://docs.ultralytics.com/hub/inference-api). Understand how to use the Inference API for running your trained models in the cloud to generate predictions. 47 | If this is a 🐛 Bug Report, please provide screenshots and steps to [reproduce](https://docs.ultralytics.com/help/minimum_reproducible_example/) your problem to help us get started working on a fix. 48 | If this is a ❓ Question, please provide as much information as possible, including dataset, model, environment details etc. so that we might provide the most helpful response. 49 | We try to respond to all issues as promptly as possible. Thank you for your patience! 50 | -------------------------------------------------------------------------------- /.github/workflows/links.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee 4 | # Ignores the following status codes to reduce false positives: 5 | # - 403(OpenVINO, 'forbidden') 6 | # - 429(Instagram, 'too many requests') 7 | # - 500(Zenodo, 'cached') 8 | # - 502(Zenodo, 'bad gateway') 9 | # - 999(LinkedIn, 'unknown status code') 10 | 11 | name: Check Broken links 12 | 13 | on: 14 | workflow_dispatch: 15 | schedule: 16 | - cron: "0 0 * * *" # runs at 00:00 UTC every day 17 | 18 | jobs: 19 | Links: 20 | runs-on: ubuntu-latest 21 | steps: 22 | - uses: actions/checkout@v4 23 | 24 | - name: Download and install lychee 25 | run: | 26 | 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) 27 | curl -L $LYCHEE_URL | tar xz -C /usr/local/bin 28 | 29 | - name: Test Markdown and HTML links with retry 30 | uses: ultralytics/actions/retry@main 31 | with: 32 | timeout_minutes: 5 33 | retry_delay_seconds: 60 34 | retries: 2 35 | run: | 36 | lychee \ 37 | --scheme 'https' \ 38 | --accept 403,429,500,502,999 \ 39 | --exclude-loopback \ 40 | --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ 41 | --exclude-path '**/ci.yml' \ 42 | --github-token ${{ secrets.GITHUB_TOKEN }} \ 43 | --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" \ 44 | './**/*.md' \ 45 | './**/*.html' | tee -a $GITHUB_STEP_SUMMARY 46 | 47 | # Raise error if broken links found 48 | if ! grep -q "0 Errors" $GITHUB_STEP_SUMMARY; then 49 | exit 1 50 | fi 51 | 52 | - name: Test Markdown, HTML, YAML, Python and Notebook links with retry 53 | if: github.event_name == 'workflow_dispatch' 54 | uses: ultralytics/actions/retry@main 55 | with: 56 | timeout_minutes: 5 57 | retry_delay_seconds: 60 58 | retries: 2 59 | run: | 60 | lychee \ 61 | --scheme 'https' \ 62 | --accept 429,999 \ 63 | --exclude-loopback \ 64 | --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ 65 | --exclude-path '**/ci.yml' \ 66 | --github-token ${{ secrets.GITHUB_TOKEN }} \ 67 | --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" \ 68 | './**/*.md' \ 69 | './**/*.html' \ 70 | './**/*.yml' \ 71 | './**/*.yaml' \ 72 | './**/*.py' \ 73 | './**/*.ipynb' | tee -a $GITHUB_STEP_SUMMARY 74 | 75 | # Raise error if broken links found 76 | if ! grep -q "0 Errors" $GITHUB_STEP_SUMMARY; then 77 | exit 1 78 | fi 79 | -------------------------------------------------------------------------------- /.github/workflows/stale.yml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | name: Close stale issues 4 | on: 5 | schedule: 6 | - cron: "0 0 * * *" # Runs at 00:00 UTC every day 7 | 8 | jobs: 9 | stale: 10 | runs-on: ubuntu-latest 11 | steps: 12 | - uses: actions/stale@v9 13 | with: 14 | repo-token: ${{ secrets.GITHUB_TOKEN }} 15 | 16 | stale-issue-message: | 17 | 👋 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. 18 | 19 | For additional resources and information, please see the links below: 20 | 21 | - **Docs**: https://docs.ultralytics.com 22 | - **HUB**: https://hub.ultralytics.com 23 | - **Community**: https://community.ultralytics.com 24 | 25 | 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! 26 | 27 | Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ 28 | 29 | stale-pr-message: | 30 | 👋 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. 31 | 32 | 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. 33 | 34 | For additional resources and information, please see the links below: 35 | 36 | - **Docs**: https://docs.ultralytics.com 37 | - **HUB**: https://hub.ultralytics.com 38 | - **Community**: https://community.ultralytics.com 39 | 40 | Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ 41 | 42 | days-before-issue-stale: 30 43 | days-before-issue-close: 10 44 | days-before-pr-stale: 90 45 | days-before-pr-close: 30 46 | exempt-issue-labels: "documentation,tutorial,TODO" 47 | operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting. 48 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # Profiling 85 | *.pclprof 86 | 87 | # pyenv 88 | .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 98 | __pypackages__/ 99 | 100 | # Celery stuff 101 | celerybeat-schedule 102 | celerybeat.pid 103 | 104 | # SageMath parsed files 105 | *.sage.py 106 | 107 | # Environments 108 | .env 109 | .venv 110 | .idea 111 | env/ 112 | venv/ 113 | ENV/ 114 | env.bak/ 115 | venv.bak/ 116 | 117 | # Spyder project settings 118 | .spyderproject 119 | .spyproject 120 | 121 | # VSCode project settings 122 | .vscode/ 123 | 124 | # Rope project settings 125 | .ropeproject 126 | 127 | # mkdocs documentation 128 | /site 129 | mkdocs_github_authors.yaml 130 | 131 | # mypy 132 | .mypy_cache/ 133 | .dmypy.json 134 | dmypy.json 135 | 136 | # Pyre type checker 137 | .pyre/ 138 | 139 | # datasets and projects 140 | datasets/ 141 | runs/ 142 | wandb/ 143 | tests/ 144 | .DS_Store 145 | 146 | # Neural Network weights ----------------------------------------------------------------------------------------------- 147 | weights/ 148 | *.weights 149 | *.pt 150 | *.pb 151 | *.onnx 152 | *.engine 153 | *.mlmodel 154 | *.mlpackage 155 | *.torchscript 156 | *.tflite 157 | *.h5 158 | *_saved_model/ 159 | *_web_model/ 160 | *_openvino_model/ 161 | *_paddle_model/ 162 | pnnx* 163 | 164 | # Autogenerated files for tests 165 | /ultralytics/assets/ 166 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published by 637 | the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |
2 |

3 | 4 | 5 |

6 | 7 | [中文](https://docs.ultralytics.com/zh/hub/) | [한국어](https://docs.ultralytics.com/ko/hub/) | [日本語](https://docs.ultralytics.com/ja/hub/) | [Русский](https://docs.ultralytics.com/ru/hub/) | [Deutsch](https://docs.ultralytics.com/de/hub/) | [Français](https://docs.ultralytics.com/fr/hub/) | [Español](https://docs.ultralytics.com/es/hub/) | [Português](https://docs.ultralytics.com/pt/hub/) | [Türkçe](https://docs.ultralytics.com/tr/hub/) | [Tiếng Việt](https://docs.ultralytics.com/vi/hub/) | [العربية](https://docs.ultralytics.com/ar/hub/) 8 | 9 | [![Ultralytics Actions](https://github.com/ultralytics/hub/actions/workflows/ci.yml/badge.svg)](https://github.com/ultralytics/hub/actions/workflows/ci.yml) 10 | [![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics) 11 | [![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/) 12 | [![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://reddit.com/r/ultralytics) 13 | Open In Colab 14 | 15 | 👋 Welcome from the [Ultralytics](https://www.ultralytics.com/) Team! We're excited to introduce [Ultralytics HUB](https://www.ultralytics.com/hub), your all-in-one platform for training, deploying, and managing [Ultralytics YOLO](https://www.ultralytics.com/yolo) models—including [YOLO11](https://docs.ultralytics.com/models/yolo11/), [YOLOv8](https://docs.ultralytics.com/models/yolov8/), [YOLOv5](https://docs.ultralytics.com/models/yolov5/), and more—from a single, intuitive interface. 🚀 16 | 17 | Explore the [Ultralytics HUB documentation](https://docs.ultralytics.com/hub/) for comprehensive guides, open [GitHub Issues](https://github.com/ultralytics/hub/issues/new/choose) for support, and join our [Discord community](https://discord.com/invite/ultralytics) for discussions and collaboration! 18 | 19 |
20 |
21 | Ultralytics GitHub 22 | 23 | Ultralytics LinkedIn 24 | 25 | Ultralytics Twitter 26 | 27 | Ultralytics YouTube 28 | 29 | Ultralytics TikTok 30 | 31 | Ultralytics BiliBili 32 | 33 | Ultralytics Discord 34 |
35 |
36 | 37 | ## 🗂️ Upload a Dataset 38 | 39 | Ultralytics HUB datasets follow the same structure and labeling conventions as [YOLO11](https://docs.ultralytics.com/models/yolo11/), [YOLOv8](https://docs.ultralytics.com/models/yolov8/), and [YOLOv5](https://docs.ultralytics.com/models/yolov5/), ensuring seamless compatibility for [object detection](https://docs.ultralytics.com/tasks/detect/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), and [image classification](https://docs.ultralytics.com/tasks/classify/) projects. 40 | 41 | - Review dataset best practices in the [Ultralytics datasets documentation](https://docs.ultralytics.com/datasets/). 42 | - Learn about dataset annotation and preparation in the [data collection and annotation guide](https://docs.ultralytics.com/guides/data-collection-and-annotation/). 43 | - Explore open-source datasets such as [COCO](https://docs.ultralytics.com/datasets/detect/coco/), [LVIS](https://docs.ultralytics.com/datasets/detect/lvis/), and [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/). 44 | 45 | ### Dataset Preparation 46 | 47 | Place your dataset YAML file in the root directory of your dataset. The YAML file, its directory, and the zipped archive should all share the same name for easy upload to [Ultralytics HUB](https://www.ultralytics.com/hub). For example, for a dataset called `coco8`, your structure should look like: 48 | 49 | - `coco8/` 50 | - `coco8.yaml` 51 | - `images/` 52 | - `labels/` 53 | - `coco8.zip` (the zipped directory for upload) 54 | 55 | You can zip your dataset using: 56 | 57 | ```bash 58 | # Zip the dataset directory for upload 59 | zip -r coco8.zip coco8 60 | ``` 61 | 62 | See [example_datasets/coco8.zip](./example_datasets/coco8.zip) for a working example. For more on dataset formats, visit the [Ultralytics datasets documentation](https://docs.ultralytics.com/datasets/). 63 | 64 |

65 | 66 |

67 | 68 | ### YAML Specifications 69 | 70 | The dataset YAML should follow the [YOLO dataset format](https://docs.ultralytics.com/guides/model-training-tips/). For detailed instructions, see the [model training tips guide](https://docs.ultralytics.com/guides/model-training-tips/). 71 | 72 | ```yaml 73 | # Example YAML configuration for a custom dataset 74 | path: ../datasets/coco8 # dataset root directory (relative or absolute) 75 | train: images/train # training images (relative to 'path') 76 | val: images/val # validation images (relative to 'path') 77 | test: # test images (optional) 78 | 79 | # Class labels 80 | names: 81 | 0: person 82 | 1: bicycle 83 | 2: car 84 | 3: motorcycle 85 | # Add more classes as needed 86 | ``` 87 | 88 | Upload your zipped dataset to [Ultralytics HUB](https://www.ultralytics.com/hub) by logging in, navigating to the [Datasets tab](https://docs.ultralytics.com/hub/datasets/), and selecting 'Upload Dataset'. You can preview and scan your dataset before training your YOLO models. 89 | 90 |

91 | Ultralytics HUB Dataset Upload Interface 92 |

93 | 94 | ## 🚀 Train a Model 95 | 96 | Connect to the Ultralytics HUB notebook and use your API key to start [training your models](https://docs.ultralytics.com/modes/train/)! Ultralytics HUB provides [cloud training](https://docs.ultralytics.com/hub/cloud-training/) for fast, scalable model development—no local GPU required. 97 | 98 | - Discover [model training tips](https://docs.ultralytics.com/guides/model-training-tips/) for best practices. 99 | - Learn about [hyperparameter tuning](https://docs.ultralytics.com/guides/hyperparameter-tuning/) to optimize your results. 100 | - Explore [experiment tracking integrations](https://docs.ultralytics.com/integrations/mlflow/) for reproducible research. 101 | 102 | 103 | Open In Colab 104 | 105 | 106 | ## 🌐 Deploy to the Real World 107 | 108 | After training, deploy your models to production using the [Export mode](https://docs.ultralytics.com/modes/export/). Ultralytics HUB supports 13+ export formats, including [TensorFlow](https://www.tensorflow.org/), [ONNX](https://onnx.ai/), [OpenVINO](https://docs.openvino.ai/latest/index.html), [CoreML](https://developer.apple.com/documentation/coreml), [PaddlePaddle](https://www.paddlepaddle.org.cn/en), and more. 109 | 110 | - Learn about [model deployment options](https://docs.ultralytics.com/guides/model-deployment-options/) for edge and cloud. 111 | - Explore [optimizing OpenVINO latency vs throughput](https://docs.ultralytics.com/guides/optimizing-openvino-latency-vs-throughput-modes/) for real-time inference. 112 | - Run your models on [iOS](https://apps.apple.com/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) devices with the [Ultralytics App](https://www.ultralytics.com/app-install). 113 | 114 | ## ❓ Have Issues or Questions? 115 | 116 | If you're new to [Ultralytics HUB](https://www.ultralytics.com/hub), the [GitHub Issues](https://github.com/ultralytics/hub/issues) tab is your go-to resource for support and bug reporting. Click 'New Issue' to share your questions, feedback, or report problems. Our team is dedicated to providing solutions and continuously improving your experience! 😃 117 | 118 | - Visit the [Ultralytics HUB FAQ](https://docs.ultralytics.com/help/FAQ/) for quick answers. 119 | - Explore the [Ultralytics help center](https://docs.ultralytics.com/help/) for troubleshooting and best practices. 120 | - Join the [Ultralytics Discord community](https://discord.com/invite/ultralytics) for real-time discussions. 121 | 122 | ## 🤝 Contribute 123 | 124 | We welcome and value your contributions! ❤️ See our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for details on how to get involved. Thank you to all our amazing contributors! 125 | 126 | [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) 127 | 128 |
129 |
130 | Ultralytics GitHub 131 | 132 | Ultralytics LinkedIn 133 | 134 | Ultralytics Twitter 135 | 136 | Ultralytics YouTube 137 | 138 | Ultralytics TikTok 139 | 140 | Ultralytics BiliBili 141 | 142 | Ultralytics Discord 143 |
144 | -------------------------------------------------------------------------------- /README.zh-CN.md: -------------------------------------------------------------------------------- 1 |
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3 | 4 | 5 |

6 | 7 | [中文](https://docs.ultralytics.com/zh/hub/) | [한국어](https://docs.ultralytics.com/ko/hub/) | [日本語](https://docs.ultralytics.com/ja/hub/) | [Русский](https://docs.ultralytics.com/ru/hub/) | [Deutsch](https://docs.ultralytics.com/de/hub/) | [Français](https://docs.ultralytics.com/fr/hub/) | [Español](https://docs.ultralytics.com/es/hub/) | [Português](https://docs.ultralytics.com/pt/hub/) | [Türkçe](https://docs.ultralytics.com/tr/hub/) | [Tiếng Việt](https://docs.ultralytics.com/vi/hub/) | [العربية](https://docs.ultralytics.com/ar/hub/) 8 | 9 | [![Ultralytics Actions](https://github.com/ultralytics/hub/actions/workflows/ci.yml/badge.svg)](https://github.com/ultralytics/hub/actions/workflows/ci.yml) 10 | [![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics) 11 | [![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/) 12 | [![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://reddit.com/r/ultralytics) 13 | Open In Colab 14 | 15 | 👋 来自 [Ultralytics](https://www.ultralytics.com/) 团队的问候！我们一直致力于打造 [Ultralytics HUB](https://www.ultralytics.com/hub)，这是一个全新平台，助您在一个中心位置轻松训练和部署所有 [Ultralytics YOLO](https://www.ultralytics.com/yolo) 模型，包括 [YOLOv5](https://docs.ultralytics.com/models/yolov5/)、[YOLOv8](https://docs.ultralytics.com/models/yolov8/) 及未来版本 🚀！ 16 | 17 | 我们希望这里的资源能帮助您充分利用 Ultralytics HUB。欢迎查阅 [Ultralytics HUB 文档](https://docs.ultralytics.com/hub/) 了解详细指南，在 [GitHub Issues](https://github.com/ultralytics/hub/issues/new/choose) 提交问题以获得支持，并加入我们的 [Discord 社区](https://discord.com/invite/ultralytics) 参与讨论！ 18 | 19 |
20 |
21 | Ultralytics GitHub 22 | 23 | Ultralytics LinkedIn 24 | 25 | Ultralytics Twitter 26 | 27 | Ultralytics YouTube 28 | 29 | Ultralytics TikTok 30 | 31 | Ultralytics BiliBili 32 | 33 | Ultralytics Discord 34 |
35 |
36 | 37 | ## 🗂️ 上传数据集 38 | 39 | Ultralytics HUB 数据集格式与 [YOLOv5](https://github.com/ultralytics/yolov5) 和 [YOLOv8](https://github.com/ultralytics/ultralytics) 完全兼容。它们采用统一的结构和标签规范，确保在 [目标检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/)、[分类](https://docs.ultralytics.com/tasks/classify/) 等任务中实现无缝体验。 40 | 41 | ### 数据集准备 42 | 43 | 请确保您的数据集根目录下包含描述数据集的 YAML 文件。准备好后，将整个目录压缩为 ZIP 文件，便于上传至 [Ultralytics HUB](https://www.ultralytics.com/hub)。YAML 文件、其所在目录及压缩包需保持同名。 44 | 45 | 例如，若您的数据集名为 'coco8'，请参考 [ultralytics/hub/example_datasets/coco8.zip](./example_datasets/coco8.zip) 的结构，在 `coco8/` 目录下放置 `coco8.yaml` 文件。然后使用如下命令将其压缩： 46 | 47 | ```bash 48 | # 压缩数据集目录以便上传 49 | zip -r coco8.zip coco8 50 | ``` 51 | 52 | 建议浏览 [example_datasets/coco8.zip](./example_datasets/coco8.zip) 获取自定义数据集构建的实际案例。更多数据集格式说明，请参阅 [Ultralytics 数据集文档](https://docs.ultralytics.com/datasets/)。 53 | 54 |

55 | 56 |

57 | 58 | ### YAML 规范 59 | 60 | 数据集 YAML 文件遵循 YOLO 格式规范。详细说明请参考 [训练自定义数据指南](https://docs.ultralytics.com/guides/model-training-tips/)。 61 | 62 | ```yaml 63 | # 自定义数据集 YAML 配置示例 64 | path: ../datasets/coco8 # 数据集根目录（相对或绝对路径） 65 | train: images/train # 训练图像（相对 'path'）8 张图像 66 | val: images/val # 验证图像（相对 'path'）8 张图像 67 | test: # 测试图像（可选） 68 | 69 | # 类别标签 70 | names: 71 | 0: person 72 | 1: bicycle 73 | 2: car 74 | 3: motorcycle 75 | # 可根据需要添加更多类别 76 | ``` 77 | 78 | 登录 [Ultralytics HUB](https://www.ultralytics.com/hub)，前往 ['Datasets' 选项卡](https://docs.ultralytics.com/hub/datasets/)，点击 'Upload Dataset' 上传您的 ZIP 数据集。上传后，您可在训练 YOLO 模型前预览和检查数据集内容。 79 | 80 |

81 | Ultralytics HUB Dataset Upload Interface 82 |

83 | 84 | ## 🚀 训练模型 85 | 86 | 通过连接 Ultralytics HUB notebook 并使用 API 密钥，即可轻松开启 [模型训练](https://docs.ultralytics.com/modes/train/)。Ultralytics HUB 提供便捷的 [云训练](https://docs.ultralytics.com/hub/cloud-training/) 功能，让您无需本地硬件即可高效训练模型。 87 | 88 | 89 | Open In Colab 90 | 91 | 92 | ## 🌐 部署到现实世界 93 | 94 | 利用 [导出模式](https://docs.ultralytics.com/modes/export/)，可将训练好的模型转换为多种格式，包括 [TensorFlow](https://www.tensorflow.org/)、[ONNX](https://onnx.ai/)、[OpenVINO](https://docs.openvino.ai/latest/index.html)、[CoreML](https://developer.apple.com/documentation/coreml)、[PaddlePaddle](https://www.paddlepaddle.org.cn/en) 等（支持 13+ 种格式）。下载 [Ultralytics App](https://www.ultralytics.com/app-install)，即可在 [iOS](https://apps.apple.com/app/ultralytics/id1583935240) 或 [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) 移动设备上直接运行您的模型！探索更多适用于边缘设备和云平台的 [模型部署选项](https://docs.ultralytics.com/guides/model-deployment-options/)。 95 | 96 | ## ❓ 有问题或疑问？ 97 | 98 | 如果您在使用 [Ultralytics HUB](https://www.ultralytics.com/hub) 过程中遇到问题，[GitHub Issues](https://github.com/ultralytics/hub/issues) 是获取支持和反馈的首选渠道。点击 'New Issue'，提交您的建议、问题或遇到的挑战。我们致力于为您持续优化体验并提供高效解决方案！😃 99 | 100 | ## 贡献 101 | 102 | 我们欢迎您的贡献！❤️ 请参阅 [贡献指南](https://docs.ultralytics.com/help/contributing/) 了解参与方式。感谢所有为 Ultralytics 生态做出贡献的开发者！ 103 | 104 | [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) 105 | 106 |
107 |
108 | Ultralytics GitHub 109 | 110 | Ultralytics LinkedIn 111 | 112 | Ultralytics Twitter 113 | 114 | Ultralytics YouTube 115 | 116 | Ultralytics TikTok 117 | 118 | Ultralytics BiliBili 119 | 120 | Ultralytics Discord 121 |
122 | -------------------------------------------------------------------------------- /example_datasets/coco8-human.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-human.zip -------------------------------------------------------------------------------- /example_datasets/coco8-human/README.md: -------------------------------------------------------------------------------- 1 | Ultralytics logo 2 | 3 | # Ultralytics coco8-human Dataset 4 | 5 | The **Ultralytics `coco8-human` dataset** is a compact, attribute-rich dataset designed for rapid testing and development within the [Ultralytics YOLO](https://github.com/ultralytics/ultralytics) ecosystem. It consists of 8 carefully selected images from the [COCO 2017 training set](https://cocodataset.org/#home), making it ideal for validating pipelines and experimenting with new features. 6 | 7 | These 8 images are split evenly, with 4 images for training and 4 for validation. Each image is annotated for the **'person'** class, including standard [bounding boxes](https://www.ultralytics.com/glossary/bounding-box) and detailed human attributes: 8 | 9 | - **Weight (kg)** 10 | - **Height (cm)** 11 | - **Gender** (0: female, 1: male) 12 | - **Age (years)** 13 | - **Ethnicity** 14 | (0: Asian, 1: White, 2: Middle Eastern, 3: Indian, 4: Latino, 5: Black) 15 | 16 | Despite its small size, the `coco8-human` dataset provides enough diversity to effectively test [object detection](https://www.ultralytics.com/glossary/object-detection) [training pipelines](https://docs.ultralytics.com/modes/train/) for errors or serve as a quick sanity check before scaling up to larger datasets such as the [full COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/). It is particularly useful for experimenting with models like [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/), [YOLOv8](https://docs.ultralytics.com/models/yolov8/), and other [YOLO model versions](https://docs.ultralytics.com/models/). 17 | 18 | This dataset is fully compatible with the Ultralytics framework and integrates seamlessly with [Ultralytics HUB](https://docs.ultralytics.com/hub/), enabling fast prototyping and validation. 19 | 20 | For more information on datasets, model training, and best practices, explore the [Ultralytics documentation](https://docs.ultralytics.com/), learn about [data annotation and collection](https://docs.ultralytics.com/guides/data-collection-and-annotation/), or discover [object detection solutions](https://www.ultralytics.com/solutions/ai-in-automotive). If you're interested in the latest trends in computer vision, check out the [Ultralytics blog](https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025). 21 | 22 | Join the discussion and get support from the community at the [Ultralytics Community Forums](https://community.ultralytics.com/) or connect with other users on [Ultralytics Discord](https://discord.com/invite/ultralytics). 23 | 24 | ## 📚 Resources 25 | 26 | Enhance your experience with COCO8-human and Ultralytics tools using these valuable resources: 27 | 28 | - [Ultralytics HUB Quickstart](https://docs.ultralytics.com/hub/quickstart/): Step-by-step guide to training models on datasets like COCO8 using Ultralytics HUB. 29 | - [Ultralytics Documentation](https://docs.ultralytics.com/): Comprehensive guides on model usage, best practices, and advanced features. 30 | - [COCO Dataset Overview](https://docs.ultralytics.com/datasets/detect/coco/): Learn more about the full COCO dataset and its applications in computer vision. 31 | - [Ultralytics Models](https://docs.ultralytics.com/models/): Explore available YOLO models for object detection, segmentation, and more. 32 | - [Ultralytics HUB GitHub Issues](https://github.com/ultralytics/hub/issues/new/choose): Report bugs, request features, or seek troubleshooting support. 33 | - [Ultralytics Discord Community](https://discord.com/invite/ultralytics): Connect with users, developers, and the Ultralytics team for real-time discussions. 34 | - [Ultralytics Community Forums](https://community.ultralytics.com/): Share your projects, ask questions, and engage with the broader Ultralytics community. 35 | - [Ultralytics YOLO11 Model Page](https://docs.ultralytics.com/models/yolo11/): Details on the latest YOLO model architecture and usage. 36 | - [Ultralytics Tasks Overview](https://docs.ultralytics.com/tasks/): Learn about supported computer vision tasks, including detection, segmentation, and more. 37 | - [Ultralytics Model Training Tips](https://docs.ultralytics.com/guides/model-training-tips/): Best practices for efficient and effective model training. 38 | - [Ultralytics Model Evaluation Insights](https://docs.ultralytics.com/guides/model-evaluation-insights/): Guidance on evaluating model performance and interpreting results. 39 | - [Ultralytics Model Deployment Options](https://docs.ultralytics.com/guides/model-deployment-options/): Explore deployment strategies for your trained models. 40 | - [Ultralytics License Information](https://www.ultralytics.com/license): For commercial use or custom solutions, complete the form to obtain an Enterprise License. 41 | 42 | ## 🚀 Contributions 43 | 44 | We welcome and value contributions from the community! If you'd like to improve the `coco8-human` dataset, suggest new features, or report issues, please visit the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) to submit a pull request or open an issue. Your feedback helps us make Ultralytics tools better for everyone. 45 | 46 | [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) 47 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/coco8-human.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # Ultralytics coco8-human dataset: 8 images from COCO train2017 annotated with person weight (kg), height (cm), gender (0: female, 1: male), age (years) and ethnicity (0: asian, 1: white, 2: middle eastern, 3: indian, 4: latino, 5: black) 4 | # Documentation: https://docs.ultralytics.com/datasets/human 5 | # Example usage: yolo train data=coco8-human.yaml 6 | # parent 7 | # ├── ultralytics 8 | # └── datasets 9 | # └── coco8-human ← downloads here (1 MB) 10 | 11 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 12 | path: # dataset root dir (leave empty for HUB) 13 | train: images/train # train images (relative to 'path') 4 images 14 | val: images/val # val images (relative to 'path') 4 images 15 | test: # test images (optional) 16 | 17 | # Classes 18 | names: 19 | 0: person 20 | 21 | # Download script/URL (optional) 22 | download: 23 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/images/train/000000000113.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-human/images/train/000000000113.jpg -------------------------------------------------------------------------------- /example_datasets/coco8-human/images/train/000000000294.jpg: 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https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-human/images/val/000000000897.jpg -------------------------------------------------------------------------------- /example_datasets/coco8-human/images/val/000000001592.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-human/images/val/000000001592.jpg -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/train/000000000113.txt: -------------------------------------------------------------------------------- 1 | 0 0.19273999333381653 0.4046249985694885 0.3646630048751831 0.7110620141029358 90 175 1 50 1 2 | 0 0.9103249907493591 0.41346099972724915 0.17935100197792053 0.5128909945487976 85 175 1 50 1 3 | 0 0.5911779999732971 0.37190601229667664 0.47019198536872864 0.6044999957084656 70 165 0 60 1 4 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/train/000000000294.txt: -------------------------------------------------------------------------------- 1 | 0 0.3283669948577881 0.5817679762840271 0.5701720118522644 0.836463987827301 95 175 1 35 1 2 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/train/000000000326.txt: -------------------------------------------------------------------------------- 1 | 0 0.5232890248298645 0.6055390238761902 0.5697969794273376 0.786346971988678 80 175 1 35 1 2 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/train/000000542449.txt: -------------------------------------------------------------------------------- 1 | 0 0.6290289759635925 0.42921900749206543 0.42336100339889526 0.7280939817428589 80 183 1 30 1 2 | 0 0.6847079992294312 0.577422022819519 0.19037599861621857 0.5224369764328003 68 175 1 30 1 3 | 0 0.8997700214385986 0.5686950087547302 0.18054300546646118 0.26351600885391235 95 178 1 45 1 4 | 0 0.11925899982452393 0.5370079874992371 0.23851799964904785 0.40976598858833313 78 183 1 25 1 5 | 0 0.9361900091171265 0.6518909931182861 0.12761999666690826 0.35350000858306885 78 178 1 25 1 6 | 0 0.4725469946861267 0.43805500864982605 0.2916490137577057 0.6874529719352722 78 180 1 28 4 7 | 0 0.16875800490379333 0.4726560115814209 0.2887890040874481 0.7135310173034668 78 183 1 28 1 -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/val/000000000241.txt: -------------------------------------------------------------------------------- 1 | 0 0.48524001240730286 0.49946099519729614 0.41756299138069153 0.9783909916877747 90 175 1 25 0 2 | 0 0.2172289937734604 0.6837109923362732 0.42845800518989563 0.543828010559082 85 175 1 28 4 3 | 0 0.7276769876480103 0.6119300127029419 0.22322900593280792 0.40179699659347534 60 165 0 30 5 4 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/val/000000000589.txt: -------------------------------------------------------------------------------- 1 | 0 0.6572269797325134 0.5764899849891663 0.22723400592803955 0.3839370012283325 75 175 1 25 1 2 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/val/000000000897.txt: -------------------------------------------------------------------------------- 1 | 0 0.6641950011253357 0.5529509782791138 0.5037660002708435 0.7146139740943909 70 175 1 30 1 2 | -------------------------------------------------------------------------------- /example_datasets/coco8-human/labels/val/000000001592.txt: -------------------------------------------------------------------------------- 1 | 0 0.5819650292396545 0.6450859904289246 0.6578590273857117 0.6047660112380981 60 165 0 30 1 2 | -------------------------------------------------------------------------------- /example_datasets/coco8-pose.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-pose.zip -------------------------------------------------------------------------------- /example_datasets/coco8-pose/README.md: -------------------------------------------------------------------------------- 1 | Ultralytics logo 2 | 3 | # Ultralytics COCO8-pose Dataset 4 | 5 | ## 🌟 Introduction 6 | 7 | Welcome to the Ultralytics COCO8-pose dataset! This compact and versatile dataset is designed for **pose estimation** tasks within the broader field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv). It contains the first 8 images from the [COCO train 2017 set](https://cocodataset.org/#home), thoughtfully divided into 4 images for training and 4 for validation. 8 | 9 | COCO8-pose is ideal for quickly testing and debugging [pose estimation](https://www.ultralytics.com/blog/pose-estimation-with-ultralytics-yolov8) models, experimenting with new [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) or [pose estimation approaches](https://arxiv.org/abs/1812.08008), and verifying the integrity of your [training pipelines](https://docs.ultralytics.com/modes/train/). Its small size (just 8 images) makes it highly manageable, while still providing enough diversity for practical sanity checks before scaling up to larger [datasets](https://docs.ultralytics.com/datasets/). 10 | 11 | This dataset is optimized for use with [Ultralytics HUB](https://hub.ultralytics.com/) and the latest [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/) models. Get started quickly by following the [Ultralytics HUB Quickstart guide](https://docs.ultralytics.com/hub/quickstart/). 12 | 13 | ## 🖼️ Sample Images and Annotations 14 | 15 | Below are examples showcasing images from the COCO8-pose dataset, visualized in a training mosaic with their corresponding pose annotations: 16 | 17 | Dataset sample image 18 | 19 | ## 📚 Resources 20 | 21 | Enhance your experience with COCO8-pose and Ultralytics tools using these valuable resources: 22 | 23 | - [Ultralytics HUB Quickstart](https://docs.ultralytics.com/hub/quickstart/): Step-by-step guide to training models on datasets like COCO8 using Ultralytics HUB. 24 | - [Ultralytics Documentation](https://docs.ultralytics.com/): Comprehensive guides on model usage, best practices, and advanced features. 25 | - [COCO Dataset Overview](https://docs.ultralytics.com/datasets/detect/coco/): Learn more about the full COCO dataset and its applications in computer vision. 26 | - [Ultralytics Models](https://docs.ultralytics.com/models/): Explore available YOLO models for object detection, segmentation, and more. 27 | - [Ultralytics HUB GitHub Issues](https://github.com/ultralytics/hub/issues/new/choose): Report bugs, request features, or seek troubleshooting support. 28 | - [Ultralytics Discord Community](https://discord.com/invite/ultralytics): Connect with users, developers, and the Ultralytics team for real-time discussions. 29 | - [Ultralytics Community Forums](https://community.ultralytics.com/): Share your projects, ask questions, and engage with the broader Ultralytics community. 30 | - [Ultralytics YOLO11 Model Page](https://docs.ultralytics.com/models/yolo11/): Details on the latest YOLO model architecture and usage. 31 | - [Ultralytics Tasks Overview](https://docs.ultralytics.com/tasks/): Learn about supported computer vision tasks, including detection, segmentation, and more. 32 | - [Ultralytics Model Training Tips](https://docs.ultralytics.com/guides/model-training-tips/): Best practices for efficient and effective model training. 33 | - [Ultralytics Model Evaluation Insights](https://docs.ultralytics.com/guides/model-evaluation-insights/): Guidance on evaluating model performance and interpreting results. 34 | - [Ultralytics Model Deployment Options](https://docs.ultralytics.com/guides/model-deployment-options/): Explore deployment strategies for your trained models. 35 | - [Ultralytics License Information](https://www.ultralytics.com/license): For commercial use or custom solutions, complete the form to obtain an Enterprise License. 36 | 37 | ## 🤝 Contributing 38 | 39 | Contributions are always welcome! If you'd like to improve this dataset or its documentation, please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/). Thank you for supporting the Ultralytics open-source community! 40 | 41 | [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) 42 | -------------------------------------------------------------------------------- /example_datasets/coco8-pose/coco8-pose.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics for HUB https://hub.ultralytics.com 4 | # Example usage: yolo train data=coco8-pose.yaml 5 | # parent 6 | # ├── ultralytics 7 | # └── datasets 8 | # └── coco8-pose ← downloads here (1 MB) 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: # dataset root dir (leave empty for HUB) 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Keypoints 17 | kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible) 18 | flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] 19 | 20 | # Classes 21 | names: 22 | 0: person 23 | 24 | # Download script/URL (optional) 25 | download: 26 | -------------------------------------------------------------------------------- /example_datasets/coco8-pose/images/train/000000000036.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-pose/images/train/000000000036.jpg -------------------------------------------------------------------------------- /example_datasets/coco8-pose/images/train/000000000049.jpg: 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0.442827 0.812500 2.000000 0.925156 0.964063 2.000000 0.507277 0.698438 2.000000 0.702703 0.942187 2.000000 0.555094 0.950000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2 | -------------------------------------------------------------------------------- /example_datasets/coco8-pose/labels/train/000000000049.txt: -------------------------------------------------------------------------------- 1 | 0 0.620039 0.593900 0.172415 0.146080 0.658793 0.552000 2.000000 0.664042 0.550000 2.000000 0.656168 0.548000 2.000000 0.679790 0.554000 2.000000 0.000000 0.000000 0.000000 0.679790 0.570000 2.000000 0.648294 0.568000 2.000000 0.674541 0.588000 2.000000 0.606299 0.582000 2.000000 0.627297 0.562000 2.000000 0.603675 0.564000 2.000000 0.627297 0.616000 2.000000 0.000000 0.000000 0.000000 0.595801 0.604000 2.000000 0.000000 0.000000 0.000000 0.569554 0.644000 2.000000 0.000000 0.000000 0.000000 2 | 0 0.385525 0.585570 0.149370 0.125860 0.414698 0.540000 1.000000 0.419948 0.538000 2.000000 0.412073 0.538000 1.000000 0.438320 0.540000 2.000000 0.000000 0.000000 0.000000 0.435696 0.558000 2.000000 0.417323 0.554000 2.000000 0.396325 0.574000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.414698 0.608000 2.000000 0.398950 0.604000 2.000000 0.372703 0.592000 2.000000 0.000000 0.000000 0.000000 0.346457 0.638000 2.000000 0.000000 0.000000 0.000000 3 | -------------------------------------------------------------------------------- /example_datasets/coco8-pose/labels/train/000000000077.txt: -------------------------------------------------------------------------------- 1 | 0 0.535530 0.308733 0.206900 0.317147 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.514000 0.194667 2.000000 0.534000 0.213333 2.000000 0.482000 0.224000 2.000000 0.526000 0.229333 2.000000 0.462000 0.186667 2.000000 0.568000 0.210667 2.000000 0.446000 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-------------------------------------------------------------------------------- /example_datasets/coco8-pose/labels/val/000000000136.txt: -------------------------------------------------------------------------------- 1 | 0 0.055220 0.651230 0.107080 0.689037 0.076000 0.483957 2.000000 0.000000 0.000000 0.000000 0.074000 0.443850 2.000000 0.000000 0.000000 0.000000 0.020000 0.457219 2.000000 0.000000 0.000000 0.000000 0.008000 0.657754 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2 | 0 0.068920 0.582019 0.137840 0.835963 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.104000 0.275401 2.000000 0.000000 0.000000 0.000000 0.088000 0.272727 2.000000 0.000000 0.000000 0.000000 0.044000 0.470588 1.000000 0.000000 0.000000 0.000000 0.028000 0.708556 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-------------------------------------------------------------------------------- /example_datasets/coco8-seg.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-seg.zip -------------------------------------------------------------------------------- /example_datasets/coco8-seg/README.md: -------------------------------------------------------------------------------- 1 | Ultralytics logo 2 | 3 | # Ultralytics COCO8-Seg Dataset 4 | 5 | ## 📄 Introduction 6 | 7 | The **Ultralytics COCO8-Seg** dataset is a compact, easy-to-use resource for **instance segmentation** tasks. It contains the first 8 images from the [COCO 2017 train set](https://cocodataset.org/#home), split into 4 training and 4 validation images. This small yet diverse dataset is ideal for quickly testing and debugging [segmentation models](https://docs.ultralytics.com/tasks/segment/) or experimenting with new [computer vision](https://en.wikipedia.org/wiki/Computer_vision) techniques. 8 | 9 | With just 8 images, COCO8-Seg is highly manageable, making it perfect for verifying [training pipelines](https://docs.ultralytics.com/guides/model-training-tips/) and catching potential issues before scaling up to [larger datasets](https://docs.ultralytics.com/datasets/). It serves as a practical sanity check for model development and experimentation. 10 | 11 | COCO8-Seg is designed for seamless integration with [Ultralytics HUB](https://hub.ultralytics.com/) and the latest [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/) models. For a deeper understanding of instance segmentation, check out our blog post: [What is Instance Segmentation? A Quick Guide](https://www.ultralytics.com/blog/what-is-instance-segmentation-a-quick-guide). 12 | 13 | ## 🖼️ Sample Images and Annotations 14 | 15 | Below are sample images from the COCO8-Seg dataset, each with its corresponding segmentation mask annotation. These examples demonstrate the data format and the types of objects included, visualized in a training mosaic. 16 | 17 | Dataset sample image 18 | 19 | ## 📚 Resources 20 | 21 | Enhance your experience with COCO8-seg and Ultralytics tools using these valuable resources: 22 | 23 | - [Ultralytics HUB Quickstart](https://docs.ultralytics.com/hub/quickstart/): Step-by-step guide to training models on datasets like COCO8 using Ultralytics HUB. 24 | - [Ultralytics Documentation](https://docs.ultralytics.com/): Comprehensive guides on model usage, best practices, and advanced features. 25 | - [COCO Dataset Overview](https://docs.ultralytics.com/datasets/detect/coco/): Learn more about the full COCO dataset and its applications in computer vision. 26 | - [Ultralytics Models](https://docs.ultralytics.com/models/): Explore available YOLO models for object detection, segmentation, and more. 27 | - [Ultralytics HUB GitHub Issues](https://github.com/ultralytics/hub/issues/new/choose): Report bugs, request features, or seek troubleshooting support. 28 | - [Ultralytics Discord Community](https://discord.com/invite/ultralytics): Connect with users, developers, and the Ultralytics team for real-time discussions. 29 | - [Ultralytics Community Forums](https://community.ultralytics.com/): Share your projects, ask questions, and engage with the broader Ultralytics community. 30 | - [Ultralytics YOLO11 Model Page](https://docs.ultralytics.com/models/yolo11/): Details on the latest YOLO model architecture and usage. 31 | - [Ultralytics Tasks Overview](https://docs.ultralytics.com/tasks/): Learn about supported computer vision tasks, including detection, segmentation, and more. 32 | - [Ultralytics Model Training Tips](https://docs.ultralytics.com/guides/model-training-tips/): Best practices for efficient and effective model training. 33 | - [Ultralytics Model Evaluation Insights](https://docs.ultralytics.com/guides/model-evaluation-insights/): Guidance on evaluating model performance and interpreting results. 34 | - [Ultralytics Model Deployment Options](https://docs.ultralytics.com/guides/model-deployment-options/): Explore deployment strategies for your trained models. 35 | - [Ultralytics License Information](https://www.ultralytics.com/license): For commercial use or custom solutions, complete the form to obtain an Enterprise License. 36 | 37 | ## 🤝 Contributing 38 | 39 | Contributions to improve the COCO8-Seg dataset or its documentation are always welcome! Please see the [Ultralytics repository](https://github.com/ultralytics/ultralytics) for contribution guidelines. Thank you for supporting open-source computer vision and helping advance the field! 40 | 41 | [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) 42 | -------------------------------------------------------------------------------- /example_datasets/coco8-seg/coco8-seg.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics for HUB https://hub.ultralytics.com 4 | # Example usage: yolo train data=coco8-seg.yaml 5 | # parent 6 | # ├── ultralytics 7 | # └── datasets 8 | # └── coco8-seg ← downloads here (1 MB) 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: # dataset root dir (leave empty for HUB) 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | names: 18 | 0: person 19 | 1: bicycle 20 | 2: car 21 | 3: motorcycle 22 | 4: airplane 23 | 5: bus 24 | 6: train 25 | 7: truck 26 | 8: boat 27 | 9: traffic light 28 | 10: fire hydrant 29 | 11: stop sign 30 | 12: parking meter 31 | 13: bench 32 | 14: bird 33 | 15: cat 34 | 16: dog 35 | 17: horse 36 | 18: sheep 37 | 19: cow 38 | 20: elephant 39 | 21: bear 40 | 22: zebra 41 | 23: giraffe 42 | 24: backpack 43 | 25: umbrella 44 | 26: handbag 45 | 27: tie 46 | 28: suitcase 47 | 29: frisbee 48 | 30: skis 49 | 31: snowboard 50 | 32: sports ball 51 | 33: kite 52 | 34: baseball bat 53 | 35: baseball glove 54 | 36: skateboard 55 | 37: surfboard 56 | 38: tennis racket 57 | 39: bottle 58 | 40: wine glass 59 | 41: cup 60 | 42: fork 61 | 43: knife 62 | 44: spoon 63 | 45: bowl 64 | 46: banana 65 | 47: apple 66 | 48: sandwich 67 | 49: orange 68 | 50: broccoli 69 | 51: carrot 70 | 52: hot dog 71 | 53: pizza 72 | 54: donut 73 | 55: cake 74 | 56: chair 75 | 57: couch 76 | 58: potted plant 77 | 59: bed 78 | 60: dining table 79 | 61: toilet 80 | 62: tv 81 | 63: laptop 82 | 64: mouse 83 | 65: remote 84 | 66: keyboard 85 | 67: cell phone 86 | 68: microwave 87 | 69: oven 88 | 70: toaster 89 | 71: sink 90 | 72: refrigerator 91 | 73: book 92 | 74: clock 93 | 75: vase 94 | 76: scissors 95 | 77: teddy bear 96 | 78: hair drier 97 | 79: toothbrush 98 | 99 | # Download script/URL (optional) 100 | download: 101 | -------------------------------------------------------------------------------- /example_datasets/coco8-seg/images/train/000000000009.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-seg/images/train/000000000009.jpg 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/example_datasets/coco8-seg/images/val/000000000049.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-seg/images/val/000000000049.jpg -------------------------------------------------------------------------------- /example_datasets/coco8-seg/images/val/000000000061.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8-seg/images/val/000000000061.jpg -------------------------------------------------------------------------------- /example_datasets/coco8-seg/labels/train/000000000009.txt: -------------------------------------------------------------------------------- 1 | 45 0.782016 0.986521 0.937078 0.874167 0.957297 0.782021 0.950562 0.739333 0.825844 0.561792 0.714609 0.420229 0.657297 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0.73528 0.319134 0.73556 0.322441 0.735 0.324278 0.73218 0.325381 0.72656 0.326115 0.72124 0.325381 0.71506 0.325381 0.70972 0.328346 0.70438 0.330184 0.70156 0.328346 0.70888 0.331286 0.71478 0.333858 0.71814 0.335354 0.7218 0.334987 0.72628 0.332388 0.72882 0.330184 0.73106 0.330184 0.73106 0.330184 0.73106 0.330184 0.73106 0.330184 0.73106 0.330184 0.73106 6 | 58 0.543123 0.94684 0.690341 0.94538 0.715512 0.90256 0.707769 0.88928 0.690341 0.87452 0.651601 0.85976 0.601234 0.85238 0.562493 0.85534 0.554751 0.85976 0.547008 0.86568 0.535381 0.88044 0.52958 0.88928 0.533438 0.9188 7 | 0 0.760367 0.71518 0.766325 0.71778 0.770157 0.72166 0.774856 0.71874 0.771444 0.71226 0.768451 0.70414 0.767192 0.69342 0.766325 0.6892 0.767192 0.68726 0.768031 0.6814 0.765066 0.67654 0.762493 0.67492 0.762913 0.66972 0.758241 0.6668 0.752283 0.67004 0.753123 0.6746 0.746299 0.67978 0.749711 0.68596 0.750131 0.68856 0.750577 0.69666 0.750997 0.70544 0.751837 0.71258 0.751417 0.71972 0.75399 0.71972 8 | 0 0.501942 0.68184 0.501312 0.67528 0.506089 0.67262 0.507979 0.66874 0.512126 0.66874 0.513727 0.67018 0.515643 0.67334 0.519764 0.67698 0.520105 0.67942 0.520105 0.68086 0.501942 0.68184 0.501627 0.68378 0.501627 0.69008 0.504488 0.693 0.507664 0.69786 0.507349 0.70416 0.503202 0.71192 0.503858 0.71556 0.507349 0.71484 0.511811 0.70294 0.509895 0.69276 0.511181 0.69252 0.513727 0.71362 0.516588 0.71436 0.515643 0.69762 0.517533 0.68426 0.501627 0.68378 9 | 0 0.925564 0.68328 0.926089 0.67936 0.920157 0.67956 0.91399 0.67778 0.910131 0.67386 0.914751 0.67014 0.920157 0.67054 0.922231 0.67308 0.92378 0.67426 0.924278 0.67328 0.925564 0.67132 0.92916 0.66856 0.93252 0.6672 0.944882 0.6668 0.946667 0.66974 0.948976 0.67446 0.946667 0.67994 0.944357 0.68446 0.944357 0.68524 0.937927 0.6825 0.926089 0.68366 10 | -------------------------------------------------------------------------------- /example_datasets/coco8-seg/labels/val/000000000061.txt: -------------------------------------------------------------------------------- 1 | 0 0.408125 0.515799 0.423156 0.53082 0.429594 0.53082 0.418141 0.533627 0.417438 0.539262 0.426016 0.537377 0.433172 0.536455 0.438188 0.523299 0.444625 0.508299 0.446766 0.502664 0.471094 0.498914 0.476813 0.499857 0.48325 0.477336 0.477531 0.443545 0.469656 0.42666 0.463938 0.421967 0.458219 0.423852 0.454641 0.432295 0.454641 0.442623 0.458922 0.453873 0.461078 0.460451 0.448906 0.479201 0.440328 0.480143 0.431031 0.485779 0.431031 0.497971 0.426734 0.508299 0.423875 0.512049 0.418859 0.516742 0.415281 0.515799 0.408844 0.513934 2 | 0 0.658422 0.510266 0.657578 0.506434 0.655078 0.496598 0.65175 0.489508 0.64425 0.479119 0.640078 0.47584 0.637578 0.476393 0.638828 0.487316 0.643422 0.496066 0.64675 0.504262 0.648422 0.509713 0.64425 0.512459 0.638 0.511352 0.636328 0.505902 0.633 0.488955 0.631328 0.481311 0.620078 0.482951 0.614672 0.475287 0.614672 0.466557 0.615922 0.455615 0.618422 0.448525 0.62175 0.444139 0.622172 0.444139 0.622172 0.435943 0.62925 0.431025 0.63425 0.432131 0.635922 0.437049 0.635922 0.445779 0.640078 0.448525 0.641328 0.452889 0.642578 0.46 0.641328 0.467643 0.641328 0.467643 0.653 0.474754 0.657578 0.483484 0.66175 0.490594 0.663 0.496066 0.665078 0.504795 0.6655 0.511352 3 | 20 0.595234 0.599795 0.589938 0.582213 0.588531 0.573422 0.578297 0.582684 0.578297 0.566496 0.603344 0.514672 0.626281 0.525307 0.637219 0.526701 0.64075 0.52207 0.648156 0.523463 0.657328 0.510041 0.668609 0.523463 0.674609 0.510963 0.690141 0.516516 0.703547 0.532705 0.708125 0.581762 0.702828 0.605348 0.679547 0.607664 0.681672 0.581291 0.673203 0.606742 0.638625 0.607213 0.627688 0.603975 0.593109 0.601189 4 | 20 0.348828 0.524754 0.351281 0.517295 0.363328 0.517992 0.375359 0.518668 0.385828 0.518668 0.397875 0.5125 0.409906 0.513176 0.418281 0.517295 0.419844 0.523484 0.420375 0.52623 0.418281 0.532398 0.426656 0.53377 0.440781 0.539262 0.453875 0.542008 0.470609 0.539959 0.477938 0.545451 0.487891 0.546127 0.491547 0.53377 0.495219 0.522111 0.509875 0.519365 0.527656 0.522111 0.536031 0.529652 0.543891 0.537889 0.550172 0.544078 0.558547 0.551619 0.569 0.566721 0.570578 0.579754 0.570047 0.591434 0.564297 0.599672 0.558016 0.603791 0.550687 0.610656 0.554875 0.627807 0.558016 0.637418 0.548594 0.651824 0.544937 0.664877 0.541797 0.671742 0.533422 0.667623 0.530281 0.658689 0.529234 0.640164 0.524 0.634672 0.499922 0.636045 0.486313 0.64084 0.48475 0.647705 0.469562 0.634672 0.463812 0.624385 0.454391 0.618893 0.441828 0.618197 0.430844 0.616148 0.424031 0.614078 0.426656 0.595553 0.420375 0.588689 0.416188 0.5825 0.412 0.563299 0.396297 0.552316 0.383219 0.553689 0.352328 0.53584 5 | 0 0.455422 0.410451 0.45525 0.418545 0.459719 0.421025 0.458172 0.424611 0.453547 0.43877 0.457141 0.443934 0.462109 0.453832 0.454922 0.465963 0.451141 0.467541 0.443609 0.467541 0.428875 0.478996 0.426813 0.485512 0.429734 0.496742 0.426813 0.50168 0.421844 0.506414 0.411219 0.513832 0.409344 0.511803 0.408656 0.508422 0.410187 0.503258 0.417047 0.496967 0.418766 0.489775 0.419797 0.475164 0.422703 0.469098 0.425781 0.463484 0.429391 0.459447 0.430234 0.455389 0.431781 0.450676 0.432641 0.450225 0.438125 0.449324 0.441891 0.445512 0.446 0.43584 0.447719 0.43 0.441719 0.427992 0.438125 0.419221 0.435375 0.412705 0.440516 0.405512 0.445312 0.402807 0.451484 0.40416 6 | -------------------------------------------------------------------------------- /example_datasets/coco8.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8.zip -------------------------------------------------------------------------------- /example_datasets/coco8/LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright 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It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published by 637 | the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /example_datasets/coco8/README.md: -------------------------------------------------------------------------------- 1 | Ultralytics logo 2 | 3 | # Ultralytics COCO8 Dataset 4 | 5 | ## 🚀 Introduction 6 | 7 | The [Ultralytics COCO8 dataset](https://docs.ultralytics.com/datasets/detect/coco8/) is a compact and versatile resource for [object detection](https://www.ultralytics.com/glossary/object-detection) tasks. It consists of the first 8 images from the popular [COCO train 2017 dataset](https://cocodataset.org/#home), split into 4 images for training and 4 for validation. This small dataset is ideal for **quick testing, debugging, and validating** your object detection pipelines within the [Ultralytics](https://www.ultralytics.com/) ecosystem. 8 | 9 | COCO8 is especially useful for verifying your setup before scaling up to [larger datasets](https://docs.ultralytics.com/datasets/), making it a valuable **sanity check** for model training, data loading, and annotation workflows. Its manageable size ensures rapid iteration, whether you are experimenting with new detection strategies or ensuring compatibility with [Ultralytics HUB](https://hub.ultralytics.com/) and [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/). To get started with training on COCO8, follow the [Ultralytics Quickstart guide](https://docs.ultralytics.com/quickstart/). 10 | 11 | ## 🖼️ Sample Images and Annotations 12 | 13 | COCO8 features diverse scenes with multiple objects, providing a realistic testing ground for object detection models. Below is a sample visualization from the dataset, displaying annotated images in a training mosaic. This highlights the variety and complexity present, even in a small dataset. 14 | 15 | Dataset sample image 16 | 17 | ## 📚 Resources 18 | 19 | Enhance your experience with COCO8 and Ultralytics tools using these valuable resources: 20 | 21 | - [Ultralytics HUB Quickstart](https://docs.ultralytics.com/hub/quickstart/): Step-by-step guide to training models on datasets like COCO8 using Ultralytics HUB. 22 | - [Ultralytics Documentation](https://docs.ultralytics.com/): Comprehensive guides on model usage, best practices, and advanced features. 23 | - [COCO Dataset Overview](https://docs.ultralytics.com/datasets/detect/coco/): Learn more about the full COCO dataset and its applications in computer vision. 24 | - [Ultralytics Models](https://docs.ultralytics.com/models/): Explore available YOLO models for object detection, segmentation, and more. 25 | - [Ultralytics HUB GitHub Issues](https://github.com/ultralytics/hub/issues/new/choose): Report bugs, request features, or seek troubleshooting support. 26 | - [Ultralytics Discord Community](https://discord.com/invite/ultralytics): Connect with users, developers, and the Ultralytics team for real-time discussions. 27 | - [Ultralytics Community Forums](https://community.ultralytics.com/): Share your projects, ask questions, and engage with the broader Ultralytics community. 28 | - [Ultralytics YOLO11 Model Page](https://docs.ultralytics.com/models/yolo11/): Details on the latest YOLO model architecture and usage. 29 | - [Ultralytics Tasks Overview](https://docs.ultralytics.com/tasks/): Learn about supported computer vision tasks, including detection, segmentation, and more. 30 | - [Ultralytics Model Training Tips](https://docs.ultralytics.com/guides/model-training-tips/): Best practices for efficient and effective model training. 31 | - [Ultralytics Model Evaluation Insights](https://docs.ultralytics.com/guides/model-evaluation-insights/): Guidance on evaluating model performance and interpreting results. 32 | - [Ultralytics Model Deployment Options](https://docs.ultralytics.com/guides/model-deployment-options/): Explore deployment strategies for your trained models. 33 | - [Ultralytics License Information](https://www.ultralytics.com/license): For commercial use or custom solutions, complete the form to obtain an Enterprise License. 34 | 35 | ## 🤝 Contributing 36 | 37 | Contributions are always welcome! If you would like to enhance the COCO8 dataset or its documentation, please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) and submit a pull request. Thank you to all our contributors! 38 | 39 | [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) 40 | -------------------------------------------------------------------------------- /example_datasets/coco8/coco8.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics for HUB https://hub.ultralytics.com 4 | # Example usage: yolo train data=coco8.yaml 5 | # parent 6 | # ├── ultralytics 7 | # └── datasets 8 | # └── coco8 ← downloads here (1 MB) 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: # dataset root dir (leave empty for HUB) 12 | train: images/train # train images (relative to 'path') 4 images 13 | val: images/val # val images (relative to 'path') 4 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | names: 18 | 0: person 19 | 1: bicycle 20 | 2: car 21 | 3: motorcycle 22 | 4: airplane 23 | 5: bus 24 | 6: train 25 | 7: truck 26 | 8: boat 27 | 9: traffic light 28 | 10: fire hydrant 29 | 11: stop sign 30 | 12: parking meter 31 | 13: bench 32 | 14: bird 33 | 15: cat 34 | 16: dog 35 | 17: horse 36 | 18: sheep 37 | 19: cow 38 | 20: elephant 39 | 21: bear 40 | 22: zebra 41 | 23: giraffe 42 | 24: backpack 43 | 25: umbrella 44 | 26: handbag 45 | 27: tie 46 | 28: suitcase 47 | 29: frisbee 48 | 30: skis 49 | 31: snowboard 50 | 32: sports ball 51 | 33: kite 52 | 34: baseball bat 53 | 35: baseball glove 54 | 36: skateboard 55 | 37: surfboard 56 | 38: tennis racket 57 | 39: bottle 58 | 40: wine glass 59 | 41: cup 60 | 42: fork 61 | 43: knife 62 | 44: spoon 63 | 45: bowl 64 | 46: banana 65 | 47: apple 66 | 48: sandwich 67 | 49: orange 68 | 50: broccoli 69 | 51: carrot 70 | 52: hot dog 71 | 53: pizza 72 | 54: donut 73 | 55: cake 74 | 56: chair 75 | 57: couch 76 | 58: potted plant 77 | 59: bed 78 | 60: dining table 79 | 61: toilet 80 | 62: tv 81 | 63: laptop 82 | 64: mouse 83 | 65: remote 84 | 66: keyboard 85 | 67: cell phone 86 | 68: microwave 87 | 69: oven 88 | 70: toaster 89 | 71: sink 90 | 72: refrigerator 91 | 73: book 92 | 74: clock 93 | 75: vase 94 | 76: scissors 95 | 77: teddy bear 96 | 78: hair drier 97 | 79: toothbrush 98 | 99 | # Download script/URL (optional) 100 | download: 101 | -------------------------------------------------------------------------------- /example_datasets/coco8/images/train/000000000009.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8/images/train/000000000009.jpg -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8/images/val/000000000049.jpg -------------------------------------------------------------------------------- /example_datasets/coco8/images/val/000000000061.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/coco8/images/val/000000000061.jpg -------------------------------------------------------------------------------- /example_datasets/coco8/labels/train/000000000009.txt: -------------------------------------------------------------------------------- 1 | 45 0.479492 0.688771 0.955609 0.5955 2 | 45 0.736516 0.247188 0.498875 0.476417 3 | 50 0.637063 0.732938 0.494125 0.510583 4 | 45 0.339438 0.418896 0.678875 0.7815 5 | 49 0.646836 0.132552 0.118047 0.0969375 6 | 49 0.773148 0.129802 0.0907344 0.0972292 7 | 49 0.668297 0.226906 0.131281 0.146896 8 | 49 0.642859 0.0792187 0.148063 0.148062 9 | -------------------------------------------------------------------------------- /example_datasets/coco8/labels/train/000000000025.txt: -------------------------------------------------------------------------------- 1 | 23 0.770336 0.489695 0.335891 0.697559 2 | 23 0.185977 0.901608 0.206297 0.129554 3 | -------------------------------------------------------------------------------- /example_datasets/coco8/labels/train/000000000030.txt: -------------------------------------------------------------------------------- 1 | 58 0.519219 0.451121 0.39825 0.75729 2 | 75 0.501188 0.592138 0.26 0.456192 3 | -------------------------------------------------------------------------------- /example_datasets/coco8/labels/train/000000000034.txt: -------------------------------------------------------------------------------- 1 | 22 0.346211 0.493259 0.689422 0.892118 2 | -------------------------------------------------------------------------------- /example_datasets/coco8/labels/val/000000000036.txt: -------------------------------------------------------------------------------- 1 | 25 0.475759 0.414523 0.951518 0.672422 2 | 0 0.671279 0.617945 0.645759 0.726859 3 | -------------------------------------------------------------------------------- /example_datasets/coco8/labels/val/000000000042.txt: -------------------------------------------------------------------------------- 1 | 16 0.606687 0.341381 0.544156 0.51 2 | -------------------------------------------------------------------------------- /example_datasets/coco8/labels/val/000000000049.txt: -------------------------------------------------------------------------------- 1 | 17 0.597835 0.63755 0.342283 0.36886 2 | 17 0.324291 0.64808 0.219711 0.3164 3 | 0 0.620039 0.5939 0.172415 0.14608 4 | 0 0.385525 0.58557 0.14937 0.12586 5 | 0 0.328898 0.70199 0.0313386 0.06714 6 | 58 0.622546 0.89961 0.185932 0.09446 7 | 0 0.760577 0.69423 0.0285564 0.05486 8 | 0 0.510709 0.69215 0.0187927 0.04682 9 | 0 0.929554 0.67602 0.0388451 0.01844 10 | -------------------------------------------------------------------------------- /example_datasets/coco8/labels/val/000000000061.txt: -------------------------------------------------------------------------------- 1 | 0 0.445688 0.480615 0.075125 0.117295 2 | 0 0.640086 0.471742 0.0508281 0.0814344 3 | 20 0.643211 0.558852 0.129828 0.097623 4 | 20 0.459703 0.592121 0.22175 0.159242 5 | 0 0.435383 0.45832 0.0534531 0.111025 6 | -------------------------------------------------------------------------------- /example_datasets/dota8.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/dota8.zip -------------------------------------------------------------------------------- /example_datasets/dota8/README.md: -------------------------------------------------------------------------------- 1 | Ultralytics logo 2 | 3 | # Ultralytics DOTA8 Dataset 4 | 5 | ## 📄 Introduction 6 | 7 | The **Ultralytics DOTA8** dataset is a compact and versatile resource for **oriented object detection (OBB)** tasks. It contains 8 images sampled from the [DOTAv1 dataset](https://captain-whu.github.io/DOTA/dataset.html), split evenly with 4 images for training and 4 for validation. DOTA8 is specifically crafted for rapid testing, debugging, and experimentation with [OBB models](https://docs.ultralytics.com/tasks/obb/) in the Ultralytics ecosystem. 8 | 9 | Despite its small size, DOTA8 offers a diverse set of scenarios, making it ideal for quickly verifying [training pipelines](https://docs.ultralytics.com/modes/train/) and ensuring your setup is error-free before scaling up to larger datasets. Its simplicity enables fast iteration and serves as a reliable sanity check for model evaluation and development. 10 | 11 | DOTA8 is fully compatible with [Ultralytics HUB](https://docs.ultralytics.com/hub/) and the latest [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/) models, empowering users to streamline their OBB workflows. 12 | 13 | ## 📚 Resources 14 | 15 | Enhance your experience with DOTA8 and Ultralytics tools using these valuable resources: 16 | 17 | - [Ultralytics HUB Quickstart](https://docs.ultralytics.com/hub/quickstart/): Step-by-step guide to training models on datasets like COCO8 using Ultralytics HUB. 18 | - [Ultralytics Documentation](https://docs.ultralytics.com/): Comprehensive guides on model usage, best practices, and advanced features. 19 | - [COCO Dataset Overview](https://docs.ultralytics.com/datasets/detect/coco/): Learn more about the full COCO dataset and its applications in computer vision. 20 | - [Ultralytics Models](https://docs.ultralytics.com/models/): Explore available YOLO models for object detection, segmentation, and more. 21 | - [Ultralytics HUB GitHub Issues](https://github.com/ultralytics/hub/issues/new/choose): Report bugs, request features, or seek troubleshooting support. 22 | - [Ultralytics Discord Community](https://discord.com/invite/ultralytics): Connect with users, developers, and the Ultralytics team for real-time discussions. 23 | - [Ultralytics Community Forums](https://community.ultralytics.com/): Share your projects, ask questions, and engage with the broader Ultralytics community. 24 | - [Ultralytics YOLO11 Model Page](https://docs.ultralytics.com/models/yolo11/): Details on the latest YOLO model architecture and usage. 25 | - [Ultralytics Tasks Overview](https://docs.ultralytics.com/tasks/): Learn about supported computer vision tasks, including detection, segmentation, and more. 26 | - [Ultralytics Model Training Tips](https://docs.ultralytics.com/guides/model-training-tips/): Best practices for efficient and effective model training. 27 | - [Ultralytics Model Evaluation Insights](https://docs.ultralytics.com/guides/model-evaluation-insights/): Guidance on evaluating model performance and interpreting results. 28 | - [Ultralytics Model Deployment Options](https://docs.ultralytics.com/guides/model-deployment-options/): Explore deployment strategies for your trained models. 29 | - [Ultralytics License Information](https://www.ultralytics.com/license): For commercial use or custom solutions, complete the form to obtain an Enterprise License. 30 | 31 | For questions about enterprise licensing or commercial applications, please see [Ultralytics Licensing](https://www.ultralytics.com/license). 32 | 33 | ## 🤝 Contributing 34 | 35 | Contributions are highly encouraged! If you would like to enhance the DOTA8 dataset or its documentation, please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) and submit a pull request. Your support helps strengthen the Ultralytics open-source community. 36 | 37 | [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) 38 | -------------------------------------------------------------------------------- /example_datasets/dota8/dota8.yaml: -------------------------------------------------------------------------------- 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license 2 | 3 | # DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics 4 | # Documentation: https://docs.ultralytics.com/datasets/obb/dota8/ 5 | # Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml 6 | # parent 7 | # ├── ultralytics 8 | # └── datasets 9 | # └── dota8 ← downloads here (1MB) 10 | 11 | train: images/train # train images (relative to 'path') 4 images 12 | val: images/val # val images (relative to 'path') 4 images 13 | 14 | # Classes for DOTA 1.0 15 | names: 16 | 0: plane 17 | 1: ship 18 | 2: storage tank 19 | 3: baseball diamond 20 | 4: tennis court 21 | 5: basketball court 22 | 6: ground track field 23 | 7: harbor 24 | 8: bridge 25 | 9: large vehicle 26 | 10: small vehicle 27 | 11: helicopter 28 | 12: roundabout 29 | 13: soccer ball field 30 | 14: swimming pool 31 | -------------------------------------------------------------------------------- /example_datasets/dota8/images/train/P0861__1024__0___1648.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ultralytics/hub/802c38446d0c429a80735c6aa3d11801c5a68c3d/example_datasets/dota8/images/train/P0861__1024__0___1648.jpg -------------------------------------------------------------------------------- /example_datasets/dota8/images/train/P1053__1024__0___90.jpg: -------------------------------------------------------------------------------- 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\n", 13 | "\n", 14 | "[中文](https://docs.ultralytics.com/zh/hub/) | [한국어](https://docs.ultralytics.com/ko/hub/) | [日本語](https://docs.ultralytics.com/ja/hub/) | [Русский](https://docs.ultralytics.com/ru/hub/) | [Deutsch](https://docs.ultralytics.com/de/hub/) | [Français](https://docs.ultralytics.com/fr/hub/) | [Español](https://docs.ultralytics.com/es/hub/) | [Português](https://docs.ultralytics.com/pt/hub/) | [Türkçe](https://docs.ultralytics.com/tr/hub/) | [Tiếng Việt](https://docs.ultralytics.com/vi/hub/) | [العربية](https://docs.ultralytics.com/ar/hub/)\n", 15 | "\n", 16 | " \"CI\n", 17 | " \"Open\n", 18 | "\n", 19 | " \"Discord\"\n", 20 | " \"Ultralytics\n", 21 | " \"Ultralytics\n", 22 | "\n", 23 | "Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook!\n", 24 | "\n", 25 | "This notebook allows you to train Ultralytics [YOLO](https://github.com/ultralytics/ultralytics) 🚀 models using [HUB](https://hub.ultralytics.com/). Please browse the HUB Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", 26 | "
" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": { 32 | "id": "eRQ2ow94MiOv" 33 | }, 34 | "source": [ 35 | "# Setup\n", 36 | "\n", 37 | "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware.\n", 38 | "\n", 39 | "[![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://www.pepy.tech/projects/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 1, 45 | "metadata": { 46 | "colab": { 47 | "base_uri": "https://localhost:8080/" 48 | }, 49 | "id": "FyDnXd-n4c7Y", 50 | "outputId": "bb836f79-9154-47c6-abc7-f1acd912ff3a" 51 | }, 52 | "outputs": [ 53 | { 54 | "output_type": "stream", 55 | "name": "stdout", 56 | "text": [ 57 | "Ultralytics 8.3.99 🚀 Python-3.11.11 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n", 58 | "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 39.6/112.6 GB disk)\n" 59 | ] 60 | } 61 | ], 62 | "source": [ 63 | "%pip install ultralytics\n", 64 | "from ultralytics import YOLO, checks, hub\n", 65 | "\n", 66 | "checks() # Verify system setup for Ultralytics training" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": { 72 | "id": "cQ9BwaAqxAm4" 73 | }, 74 | "source": [ 75 | "# Start\n", 76 | "\n", 77 | "⚡ Login with your API key, load your YOLO 🚀 model, and start training in 3 lines of code!" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": { 84 | "id": "XSlZaJ9Iw_iZ" 85 | }, 86 | "outputs": [], 87 | "source": [ 88 | "# Login to HUB using your API key (https://hub.ultralytics.com/settings?tab=api+keys)\n", 89 | "hub.login(\"YOUR_API_KEY\")\n", 90 | "\n", 91 | "# Load your model from HUB (replace 'YOUR_MODEL_ID' with your model ID)\n", 92 | "model = YOLO(\"https://hub.ultralytics.com/models/YOUR_MODEL_ID\")\n", 93 | "\n", 94 | "# Train the model\n", 95 | "results = model.train()" 96 | ] 97 | } 98 | ], 99 | "metadata": { 100 | "accelerator": "GPU", 101 | "colab": { 102 | "name": "Ultralytics HUB", 103 | "provenance": [] 104 | }, 105 | "kernelspec": { 106 | "display_name": "Python 3", 107 | "name": "python3" 108 | }, 109 | "language_info": { 110 | "name": "python" 111 | } 112 | }, 113 | "nbformat": 4, 114 | "nbformat_minor": 0 115 | } 116 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # Ultralytics requirements 2 | # Usage: pip install -r requirements.txt 3 | 4 | ultralytics>=8.0.232 5 | --------------------------------------------------------------------------------