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workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/ci-testing.yml/master?enable=pin","Warn: GitHub-owned GitHubAction not pinned by hash: .github/workflows/ci-testing.yml:29: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/ci-testing.yml/master?enable=pin","Warn: GitHub-owned GitHubAction not pinned by hash: .github/workflows/ci-testing.yml:30: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/ci-testing.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/ci-testing.yml:33: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/ci-testing.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/cla.yml:30: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/cla.yml/master?enable=pin","Warn: GitHub-owned GitHubAction not pinned by hash: .github/workflows/docker.yml:19: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/docker.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/docker.yml:24: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/docker.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/docker.yml:27: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/docker.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/docker.yml:30: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/docker.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/docker.yml:36: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/docker.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/docker.yml:46: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/docker.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/docker.yml:55: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/docker.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/format.yml:25: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/format.yml/master?enable=pin","Warn: GitHub-owned GitHubAction not pinned by hash: .github/workflows/links.yml:22: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/links.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/links.yml:28: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/links.yml/master?enable=pin","Warn: third-party GitHubAction not pinned by hash: .github/workflows/links.yml:54: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/links.yml/master?enable=pin","Warn: GitHub-owned GitHubAction not pinned by hash: .github/workflows/merge-main-into-prs.yml:20: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/merge-main-into-prs.yml/master?enable=pin","Warn: GitHub-owned GitHubAction not pinned by hash: .github/workflows/merge-main-into-prs.yml:23: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/merge-main-into-prs.yml/master?enable=pin","Warn: GitHub-owned GitHubAction not pinned by hash: .github/workflows/stale.yml:12: update your workflow using https://app.stepsecurity.io/secureworkflow/ultralytics/yolov5/stale.yml/master?enable=pin","Warn: containerImage not pinned by hash: utils/docker/Dockerfile:7: pin your Docker image by updating 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pipCommand not pinned by hash: utils/docker/Dockerfile-arm64:21","Warn: pipCommand not pinned by hash: utils/docker/Dockerfile-arm64:22-23","Warn: pipCommand not pinned by hash: utils/docker/Dockerfile-cpu:23","Warn: pipCommand not pinned by hash: utils/docker/Dockerfile-cpu:24-27","Warn: pipCommand not pinned by hash: utils/google_app_engine/Dockerfile:18","Warn: downloadThenRun not pinned by hash: data/scripts/get_imagenet.sh:45","Warn: pipCommand not pinned by hash: utils/aws/userdata.sh:16","Warn: pipCommand not pinned by hash: utils/aws/userdata.sh:16","Warn: pipCommand not pinned by hash: .github/workflows/merge-main-into-prs.yml:29","Info:   0 out of   9 GitHub-owned GitHubAction dependencies pinned","Info:   0 out of  13 third-party GitHubAction dependencies pinned","Info:   0 out of   4 containerImage dependencies pinned","Info:   0 out of  10 pipCommand dependencies pinned","Info:   0 out of   1 downloadThenRun dependencies pinned"],"documentation":{"short":"Determines if the 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GHSA-q34m-jh98-gwm2"],"documentation":{"short":"Determines if the project has open, known unfixed 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Machine Learning \u0026 AI","The Data Science Toolbox","Summary","语言资源库","PyTorch Models","**Vision Models**","Application Recommendation","Object Detection Models","Video AI/ML","Repos","🤖 AI \u0026 Machine Learning","Research \u0026 Data Analysis","⚙️ LLM Inference \u0026 Serving (15)"],"sub_categories":["Other","Scientific Computing","Uncategorized","Tools","Deep Learning Packages","python","Computer Vision","🧠 AI Applications","Rust"],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cp\u003e\n    \u003ca href=\"https://www.ultralytics.com/yolo/yolo27?utm_source=github\u0026utm_medium=social\u0026utm_campaign=yolo27-launch-2026\u0026utm_content=banner\" target=\"_blank\"\u003e\n      \u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\" alt=\"Ultralytics YOLO banner\"\u003e\u003c/a\u003e\n  \u003c/p\u003e\n\n[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)\n\n\u003cdiv\u003e\n    \u003ca href=\"https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml\"\u003e\u003cimg src=\"https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg\" alt=\"YOLOv5 CI Testing\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://hub.docker.com/r/ultralytics/yolov5\"\u003e\u003cimg src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord\u0026logoColor=white\u0026label=Discord\u0026color=blue\"\u003e\u003c/a\u003e \u003ca href=\"https://community.ultralytics.com\"\u003e\u003cimg alt=\"Ultralytics Forums\" src=\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com\u0026logo=discourse\u0026label=Forums\u0026color=blue\"\u003e\u003c/a\u003e \u003ca href=\"https://www.reddit.com/r/ultralytics/\"\u003e\u003cimg alt=\"Ultralytics Reddit\" src=\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat\u0026logo=reddit\u0026logoColor=white\u0026label=Reddit\u0026color=blue\"\u003e\u003c/a\u003e\n    \u003cbr\u003e\n    \u003ca href=\"https://console.paperspace.com/github/ultralytics/yolov5\"\u003e\u003cimg src=\"https://img.shields.io/badge/Run%20on-Gradient-0A0A0A\" alt=\"Run on Gradient\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://www.kaggle.com/models/ultralytics/yolov5\"\u003e\u003cimg src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"\u003e\u003c/a\u003e\n  \u003c/div\u003e\n  \u003cbr\u003e\n\nUltralytics YOLOv5 🚀 is a fast, accurate, and easy-to-use computer vision model developed by [Ultralytics](https://www.ultralytics.com). Based on the [PyTorch](https://pytorch.org/) framework, YOLOv5 is renowned for its speed, accuracy, and simplicity. It incorporates insights and best practices from extensive research and development, making it a popular and reliable choice for a wide range of vision AI tasks, including [object detection](https://docs.ultralytics.com/tasks/detect), [image segmentation](https://docs.ultralytics.com/tasks/segment), and [image classification](https://docs.ultralytics.com/tasks/classify).\n\nWe hope the resources here help you get the most out of YOLOv5. Please browse the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) for detailed information, raise an issue on [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) for support, and join our [Discord community](https://discord.com/invite/ultralytics) for questions and discussions!\n\nTo request an Enterprise License, please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"2%\" alt=\"Ultralytics GitHub\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"2%\" alt=\"Ultralytics LinkedIn\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"2%\" alt=\"Ultralytics Twitter\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.youtube.com/ultralytics?sub_confirmation=1\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"2%\" alt=\"Ultralytics YouTube\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"2%\" alt=\"Ultralytics TikTok\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://ultralytics.com/bilibili\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png\" width=\"2%\" alt=\"Ultralytics BiliBili\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png\" width=\"2%\" alt=\"Ultralytics Discord\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003c/div\u003e\n\u003cbr\u003e\n\n## 🚀 Explore the Ultralytics YOLO Ecosystem\n\nYOLOv5 is a mature, production-proven model that remains an excellent choice for fast and reliable [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). If your project calls for the newest architectures, additional tasks such as [pose estimation](https://docs.ultralytics.com/tasks/pose) and [oriented object detection (OBB)](https://docs.ultralytics.com/tasks/obb), or a unified Python and CLI interface, the actively maintained [`ultralytics`](https://github.com/ultralytics/ultralytics) package brings the latest [Ultralytics YOLO models](https://docs.ultralytics.com/models) together in one place. Explore the [Ultralytics Docs](https://docs.ultralytics.com) to find the best fit for your use case.\n\n```bash\n# Install the ultralytics package for the latest Ultralytics YOLO models\npip install ultralytics\n```\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://docs.ultralytics.com/models\" target=\"_blank\"\u003e\n  \u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png\" alt=\"Ultralytics YOLO performance comparison\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## 📚 Documentation\n\nSee the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) for full documentation on training, testing, and deployment. See below for quickstart examples.\n\n\u003cdetails open\u003e\n\u003csummary\u003eInstall\u003c/summary\u003e\n\nClone the repository and install dependencies in a [**Python\u003e=3.8.0**](https://www.python.org/) environment. Ensure you have [**PyTorch\u003e=1.8**](https://pytorch.org/get-started/locally/) installed.\n\n```bash\n# Clone the YOLOv5 repository\ngit clone https://github.com/ultralytics/yolov5\n\n# Navigate to the cloned directory\ncd yolov5\n\n# Install required packages\npip install -r requirements.txt\n```\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eInference with PyTorch Hub\u003c/summary\u003e\n\nUse YOLOv5 via [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch-hub-model-loading) for inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) are automatically downloaded from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).\n\n```python\nimport torch\n\n# Load a YOLOv5 model (options: yolov5n, yolov5s, yolov5m, yolov5l, yolov5x)\nmodel = torch.hub.load(\"ultralytics/yolov5\", \"yolov5s\")  # Default: yolov5s\n\n# Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list)\nimg = \"https://ultralytics.com/images/zidane.jpg\"  # Example image\n\n# Perform inference (handles batching, resizing, normalization automatically)\nresults = model(img)\n\n# Process the results (options: .print(), .show(), .save(), .crop(), .pandas())\nresults.print()  # Print results to console\nresults.show()  # Display results in a window\nresults.save()  # Save results to runs/detect/exp\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eInference with detect.py\u003c/summary\u003e\n\nThe `detect.py` script runs inference on various sources. It automatically downloads [models](https://github.com/ultralytics/yolov5/tree/master/models) from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saves the results to the `runs/detect` directory.\n\n```bash\n# Run inference using a webcam\npython detect.py --weights yolov5s.pt --source 0\n\n# Run inference on a local image file\npython detect.py --weights yolov5s.pt --source img.jpg\n\n# Run inference on a local video file\npython detect.py --weights yolov5s.pt --source vid.mp4\n\n# Run inference on a screen capture\npython detect.py --weights yolov5s.pt --source screen\n\n# Run inference on a directory of images\npython detect.py --weights yolov5s.pt --source path/to/images/\n\n# Run inference on a text file listing image paths\npython detect.py --weights yolov5s.pt --source list.txt\n\n# Run inference on a text file listing stream URLs\npython detect.py --weights yolov5s.pt --source list.streams\n\n# Run inference using a glob pattern for images\npython detect.py --weights yolov5s.pt --source 'path/to/*.jpg'\n\n# Run inference on a YouTube video URL\npython detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4'\n\n# Run inference on an RTSP, RTMP, or HTTP stream\npython detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4'\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eTraining\u003c/summary\u003e\n\nThe commands below demonstrate how to reproduce YOLOv5 [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco) results. Both [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) are downloaded automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are approximately 1/2/4/6/8 days on a single [NVIDIA V100 GPU](https://www.nvidia.com/en-us/data-center/v100/). Using [Multi-GPU training](https://docs.ultralytics.com/yolov5/tutorials/multi-gpu-training) can significantly reduce training time. Use the largest `--batch-size` your hardware allows, or use `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). The batch sizes shown below are for V100-16GB GPUs.\n\n```bash\n# Train YOLOv5n on COCO for 300 epochs\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128\n\n# Train YOLOv5s on COCO for 300 epochs\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64\n\n# Train YOLOv5m on COCO for 300 epochs\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40\n\n# Train YOLOv5l on COCO for 300 epochs\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24\n\n# Train YOLOv5x on COCO for 300 epochs\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16\n```\n\n\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png\" alt=\"YOLOv5 Training Results\"\u003e\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eTutorials\u003c/summary\u003e\n\n- **[Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train-custom-data)** 🚀 **RECOMMENDED**: Learn how to train YOLOv5 on your own datasets.\n- **[Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips)** ☘️: Improve your model's performance with expert tips.\n- **[Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi-gpu-training)**: Speed up training using multiple GPUs.\n- **[PyTorch Hub Integration](https://docs.ultralytics.com/yolov5/tutorials/pytorch-hub-model-loading)**: Easily load models using PyTorch Hub.\n- **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model-export)** 🚀: Convert your models to various deployment formats like [ONNX](https://onnx.ai/) or [TensorRT](https://developer.nvidia.com/tensorrt).\n- **[NVIDIA Jetson Deployment](https://docs.ultralytics.com/guides/nvidia-jetson)**: Deploy YOLOv5 on [NVIDIA Jetson](https://developer.nvidia.com/embedded-computing) devices.\n- **[Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test-time-augmentation)**: Enhance prediction accuracy with TTA.\n- **[Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model-ensembling)**: Combine multiple models for better performance.\n- **[Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model-pruning-and-sparsity)**: Optimize models for size and speed.\n- **[Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter-evolution)**: Automatically find the best training hyperparameters.\n- **[Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer-learning-with-frozen-layers)**: Adapt pretrained models to new tasks efficiently using [transfer learning](https://www.ultralytics.com/glossary/transfer-learning).\n- **[Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture-description)**: Understand the YOLOv5 model architecture.\n- **[Ultralytics Platform Training](https://platform.ultralytics.com)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics Platform.\n- **[ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml-logging-integration)**: Integrate with [ClearML](https://clear.ml/) for experiment tracking.\n- **[Neural Magic DeepSparse Integration](https://docs.ultralytics.com/yolov5/tutorials/neural-magic-pruning-quantization)**: Accelerate inference with DeepSparse.\n- **[Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet-logging-integration)**: Log experiments using [Comet ML](https://www.comet.com/site/).\n\n\u003c/details\u003e\n\n## 🧩 Integrations\n\nOur key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights \u0026 Biases](https://docs.ultralytics.com/integrations/weights-biases), [Comet ML](https://docs.ultralytics.com/integrations/comet), [Roboflow](https://docs.ultralytics.com/integrations/roboflow), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations).\n\n\u003ca href=\"https://platform.ultralytics.com\" target=\"_blank\"\u003e\n    \u003cimg width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\" alt=\"Ultralytics active learning integrations\"\u003e\n\u003c/a\u003e\n\n## 🤔 Why YOLOv5?\n\nYOLOv5 is designed for simplicity and ease of use. We prioritize real-world performance and accessibility.\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png\" alt=\"YOLOv5 Performance Chart\"\u003e\u003c/p\u003e\n\u003cdetails\u003e\n  \u003csummary\u003eYOLOv5-P5 640 Figure\u003c/summary\u003e\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png\" alt=\"YOLOv5 P5 640 Performance Chart\"\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n  \u003csummary\u003eFigure Notes\u003c/summary\u003e\n\n- **COCO AP val** denotes the [mean Average Precision (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map) at [Intersection over Union (IoU)](https://www.ultralytics.com/glossary/intersection-over-union-iou) thresholds from 0.5 to 0.95, measured on the 5,000-image [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco) across various inference sizes (256 to 1536 pixels).\n- **GPU Speed** measures the average inference time per image on the [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco) using an [AWS p3.2xlarge V100 instance](https://aws.amazon.com/ec2/instance-types/p4/) with a batch size of 32.\n- **EfficientDet** data is sourced from the [google/automl repository](https://github.com/google/automl) at batch size 8.\n- **Reproduce** these results using the command: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`\n\n\u003c/details\u003e\n\n### Pretrained Checkpoints\n\nThis table shows the performance metrics for various YOLOv5 models trained on the COCO dataset.\n\n| Model                                                                                                                                                                   | Size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003eval\u003cbr\u003e50-95 | mAP\u003csup\u003eval\u003cbr\u003e50 | Speed\u003cbr\u003e\u003csup\u003eCPU b1\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eV100 b1\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eV100 b32\u003cbr\u003e(ms) | Params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e@640 (B) |\n| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |\n| [YOLOv5n](https://platform.ultralytics.com/ultralytics/yolov5/yolov5nu)                                                                                                 | 640                   | 28.0                 | 45.7              | **45**                       | **6.3**                       | **0.6**                        | **1.9**            | **4.5**                |\n| [YOLOv5s](https://platform.ultralytics.com/ultralytics/yolov5/yolov5su)                                                                                                 | 640                   | 37.4                 | 56.8              | 98                           | 6.4                           | 0.9                            | 7.2                | 16.5                   |\n| [YOLOv5m](https://platform.ultralytics.com/ultralytics/yolov5/yolov5mu)                                                                                                 | 640                   | 45.4                 | 64.1              | 224                          | 8.2                           | 1.7                            | 21.2               | 49.0                   |\n| [YOLOv5l](https://platform.ultralytics.com/ultralytics/yolov5/yolov5lu)                                                                                                 | 640                   | 49.0                 | 67.3              | 430                          | 10.1                          | 2.7                            | 46.5               | 109.1                  |\n| [YOLOv5x](https://platform.ultralytics.com/ultralytics/yolov5/yolov5xu)                                                                                                 | 640                   | 50.7                 | 68.9              | 766                          | 12.1                          | 4.8                            | 86.7               | 205.7                  |\n|                                                                                                                                                                         |                       |                      |                   |                              |                               |                                |                    |                        |\n| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt)                                                                                    | 1280                  | 36.0                 | 54.4              | 153                          | 8.1                           | 2.1                            | 3.2                | 4.6                    |\n| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt)                                                                                    | 1280                  | 44.8                 | 63.7              | 385                          | 8.2                           | 3.6                            | 12.6               | 16.8                   |\n| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt)                                                                                    | 1280                  | 51.3                 | 69.3              | 887                          | 11.1                          | 6.8                            | 35.7               | 50.0                   |\n| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt)                                                                                    | 1280                  | 53.7                 | 71.3              | 1784                         | 15.8                          | 10.5                           | 76.8               | 111.4                  |\n| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)\u003cbr\u003e+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test-time-augmentation) | 1280\u003cbr\u003e1536          | 55.0\u003cbr\u003e**55.8**     | 72.7\u003cbr\u003e**72.7**  | 3136\u003cbr\u003e-                    | 26.2\u003cbr\u003e-                     | 19.4\u003cbr\u003e-                      | 140.7\u003cbr\u003e-         | 209.8\u003cbr\u003e-             |\n\n\u003cdetails\u003e\n  \u003csummary\u003eTable Notes\u003c/summary\u003e\n\n- The 640-pixel rows report the legacy anchor-based YOLOv5 checkpoints, while their links open the corresponding anchor-free YOLOv5u successor models on Ultralytics Platform. P6 links continue to download the legacy 1280-pixel checkpoints directly.\n- All checkpoints were trained for 300 epochs using default settings. Nano (n) and Small (s) models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyperparameters, while Medium (m), Large (l), and Extra-Large (x) models use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).\n- **mAP\u003csup\u003eval\u003c/sup\u003e** values represent single-model, single-scale performance on the [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco).\u003cbr\u003eReproduce using: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`\n- **Speed** metrics are averaged over COCO val images using an [AWS p3.2xlarge V100 instance](https://aws.amazon.com/ec2/instance-types/p4/). Non-Maximum Suppression (NMS) time (~1 ms/image) is not included.\u003cbr\u003eReproduce using: `python val.py --data coco.yaml --img 640 --task speed --batch 1`\n- **TTA** ([Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test-time-augmentation)) includes reflection and scale augmentations for improved accuracy.\u003cbr\u003eReproduce using: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`\n\n\u003c/details\u003e\n\n## 🖼️ Segmentation\n\nThe YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) introduced [instance segmentation](https://docs.ultralytics.com/tasks/segment) models designed for easy training, validation, and deployment. For full details, see the [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and explore the [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart examples.\n\n\u003cdetails\u003e\n  \u003csummary\u003eSegmentation Checkpoints\u003c/summary\u003e\n\n\u003cdiv align=\"center\"\u003e\n\u003ca align=\"center\" href=\"https://www.ultralytics.com/yolo\" target=\"_blank\"\u003e\n\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png\" alt=\"YOLOv5 Segmentation Performance Chart\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\nYOLOv5 segmentation models were trained on the [COCO dataset](https://docs.ultralytics.com/datasets/segment/coco) for 300 epochs at an image size of 640 pixels using A100 GPUs. Models were exported to [ONNX](https://onnx.ai/) FP32 for CPU speed tests and [TensorRT](https://developer.nvidia.com/tensorrt) FP16 for GPU speed tests. All speed tests were conducted on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for reproducibility.\n\n| Model                                                                                      | Size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003ebox\u003cbr\u003e50-95 | mAP\u003csup\u003emask\u003cbr\u003e50-95 | Train Time\u003cbr\u003e\u003csup\u003e300 epochs\u003cbr\u003eA100 (hours) | Speed\u003cbr\u003e\u003csup\u003eONNX CPU\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eTRT A100\u003cbr\u003e(ms) | Params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e@640 (B) |\n| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |\n| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                   | 27.6                 | 23.4                  | 80:17                                         | **62.7**                       | **1.2**                        | **2.0**            | **7.1**                |\n| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                   | 37.6                 | 31.7                  | 88:16                                         | 173.3                          | 1.4                            | 7.6                | 26.4                   |\n| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                   | 45.0                 | 37.1                  | 108:36                                        | 427.0                          | 2.2                            | 22.0               | 70.8                   |\n| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640                   | 49.0                 | 39.9                  | 66:43 (2x)                                    | 857.4                          | 2.9                            | 47.9               | 147.7                  |\n| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640                   | **50.7**             | **41.4**              | 62:56 (3x)                                    | 1579.2                         | 4.5                            | 88.8               | 265.7                  |\n\n- All checkpoints were trained for 300 epochs using the SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at an image size of 640 pixels, using default settings.\u003cbr\u003eTraining runs are logged at [https://wandb.ai/glenn-jocher/YOLOv5_v70_official](https://wandb.ai/glenn-jocher/YOLOv5_v70_official).\n- **Accuracy** values represent single-model, single-scale performance on the COCO dataset.\u003cbr\u003eReproduce using: `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`\n- **Speed** metrics are averaged over 100 inference images using a [Colab Pro A100 High-RAM instance](https://colab.research.google.com/signup). Values indicate inference speed only (NMS adds approximately 1ms per image).\u003cbr\u003eReproduce using: `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`\n- **Export** to ONNX (FP32) and TensorRT (FP16) was performed using `export.py`.\u003cbr\u003eReproduce using: `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003eSegmentation Usage Examples \u0026nbsp;\u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\u003c/summary\u003e\n\n### Train\n\nYOLOv5 segmentation training supports automatic download of the [COCO128-seg dataset](https://docs.ultralytics.com/datasets/segment/coco128-seg) via the `--data coco128-seg.yaml` argument. For the full [COCO-segments dataset](https://docs.ultralytics.com/datasets/segment/coco), download it manually using `bash data/scripts/get_coco.sh --train --val --segments` and then train with `python segment/train.py --data coco.yaml`.\n\n```bash\n# Train on a single GPU\npython segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640\n\n# Train using Multi-GPU Distributed Data Parallel (DDP)\npython -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3\n```\n\n### Val\n\nValidate the mask [mean Average Precision (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map) of YOLOv5s-seg on the COCO dataset:\n\n```bash\n# Download COCO validation segments split (780MB, 5000 images)\nbash data/scripts/get_coco.sh --val --segments\n\n# Validate the model\npython segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640\n```\n\n### Predict\n\nUse the pretrained YOLOv5m-seg.pt model to perform segmentation on `bus.jpg`:\n\n```bash\n# Run prediction\npython segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg\n```\n\n```python\n# Load model from PyTorch Hub (Note: Inference support might vary)\nmodel = torch.hub.load(\"ultralytics/yolov5\", \"custom\", \"yolov5m-seg.pt\")\n```\n\n| ![Zidane Segmentation Example](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![Bus Segmentation Example](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |\n| :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |\n\n### Export\n\nExport the YOLOv5s-seg model to ONNX and TensorRT formats:\n\n```bash\n# Export model\npython export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0\n```\n\n\u003c/details\u003e\n\n## 🏷️ Classification\n\nYOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases/v6.2) introduced support for [image classification](https://docs.ultralytics.com/tasks/classify) model training, validation, and deployment. Check the [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) for details and the [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart guides.\n\n\u003cdetails\u003e\n  \u003csummary\u003eClassification Checkpoints\u003c/summary\u003e\n\n\u003cbr\u003e\n\nYOLOv5-cls classification models were trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) for 90 epochs using a 4xA100 instance. [ResNet](https://arxiv.org/abs/1512.03385) and [EfficientNet](https://arxiv.org/abs/1905.11946) models were trained alongside under identical settings for comparison. Models were exported to [ONNX](https://onnx.ai/) FP32 (CPU speed tests) and [TensorRT](https://developer.nvidia.com/tensorrt) FP16 (GPU speed tests). All speed tests were run on Google [Colab Pro](https://colab.research.google.com/signup) for reproducibility.\n\n| Model                                                                                              | Size\u003cbr\u003e\u003csup\u003e(pixels) | Acc\u003cbr\u003e\u003csup\u003etop1 | Acc\u003cbr\u003e\u003csup\u003etop5 | Training\u003cbr\u003e\u003csup\u003e90 epochs\u003cbr\u003e4xA100 (hours) | Speed\u003cbr\u003e\u003csup\u003eONNX CPU\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eTensorRT V100\u003cbr\u003e(ms) | Params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e@224 (B) |\n| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |\n| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                        | **0.5**                             | **2.5**            | **0.5**                |\n| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                            | 0.6                                 | 5.4                | 1.4                    |\n| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                           | 0.9                                 | 12.9               | 3.9                    |\n| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                           | 1.4                                 | 26.5               | 8.5                    |\n| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                           | 1.8                                 | 48.1               | 15.9                   |\n|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |\n| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                           | 0.5                                 | 11.7               | 3.7                    |\n| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt)               | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                           | 0.9                                 | 21.8               | 7.4                    |\n| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                           | 1.0                                 | 25.6               | 8.5                    |\n| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                           | 1.9                                 | 44.5               | 15.9                   |\n|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |\n| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                           | 1.3                                 | 5.3                | 1.0                    |\n| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                           | 1.6                                 | 7.8                | 1.5                    |\n| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                           | 1.6                                 | 9.1                | 1.7                    |\n| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                           | 1.9                                 | 12.2               | 2.4                    |\n\n\u003cdetails\u003e\n  \u003csummary\u003eTable Notes (click to expand)\u003c/summary\u003e\n\n- All checkpoints were trained for 90 epochs using the SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at an image size of 224 pixels, using default settings.\u003cbr\u003eTraining runs are logged at [https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2](https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2).\n- **Accuracy** values (top-1 and top-5) represent single-model, single-scale performance on the [ImageNet-1k dataset](https://docs.ultralytics.com/datasets/classify/imagenet).\u003cbr\u003eReproduce using: `python classify/val.py --data ../datasets/imagenet --img 224`\n- **Speed** metrics are averaged over 100 inference images using a Google [Colab Pro V100 High-RAM instance](https://colab.research.google.com/signup).\u003cbr\u003eReproduce using: `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`\n- **Export** to ONNX (FP32) and TensorRT (FP16) was performed using `export.py`.\u003cbr\u003eReproduce using: `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`\n\n\u003c/details\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003eClassification Usage Examples \u0026nbsp;\u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\u003c/summary\u003e\n\n### Train\n\nYOLOv5 classification training supports automatic download for datasets like [MNIST](https://docs.ultralytics.com/datasets/classify/mnist), [Fashion-MNIST](https://docs.ultralytics.com/datasets/classify/fashion-mnist), [CIFAR10](https://docs.ultralytics.com/datasets/classify/cifar10), [CIFAR100](https://docs.ultralytics.com/datasets/classify/cifar100), [Imagenette](https://docs.ultralytics.com/datasets/classify/imagenette), [Imagewoof](https://docs.ultralytics.com/datasets/classify/imagewoof), and [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) using the `--data` argument. For example, start training on MNIST with `--data mnist`.\n\n```bash\n# Train on a single GPU using CIFAR-100 dataset\npython classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128\n\n# Train using Multi-GPU DDP on ImageNet dataset\npython -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3\n```\n\n### Val\n\nValidate the accuracy of the YOLOv5m-cls model on the ImageNet-1k validation dataset:\n\n```bash\n# Download ImageNet validation split (6.3GB, 50,000 images)\nbash data/scripts/get_imagenet.sh --val\n\n# Validate the model\npython classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224\n```\n\n### Predict\n\nUse the pretrained YOLOv5s-cls.pt model to classify the image `bus.jpg`:\n\n```bash\n# Run prediction\npython classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg\n```\n\n```python\n# Load model from PyTorch Hub\nmodel = torch.hub.load(\"ultralytics/yolov5\", \"custom\", \"yolov5s-cls.pt\")\n```\n\n### Export\n\nExport trained YOLOv5s-cls, ResNet50, and EfficientNet_b0 models to ONNX and TensorRT formats:\n\n```bash\n# Export models\npython export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224\n```\n\n\u003c/details\u003e\n\n## ☁️ Environments\n\nGet started quickly with our pre-configured environments. Click the icons below for setup details.\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://console.paperspace.com/github/ultralytics/yolov5\" title=\"Run on Paperspace Gradient\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" title=\"Open in Google Colab\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.kaggle.com/models/ultralytics/yolov5\" title=\"Open in Kaggle\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://hub.docker.com/r/ultralytics/yolov5\" title=\"Pull Docker Image\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/yolov5/environments/aws-quickstart-tutorial\" title=\"AWS Quickstart Guide\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/yolov5/environments/google-cloud-quickstart-tutorial\" title=\"GCP Quickstart Guide\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## 🤝 Contribute\n\nWe welcome your contributions! Making YOLOv5 accessible and effective is a community effort. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. Share your feedback through the [YOLOv5 Survey](https://www.ultralytics.com/survey?utm_source=github\u0026utm_medium=social\u0026utm_campaign=Survey). Thank you to all our contributors for making YOLOv5 better!\n\n[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors)\n\n## 📜 License\n\nUltralytics provides two licensing options to meet different needs:\n\n- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-3.0) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.\n- **Enterprise License**: Tailored for commercial applications, this license allows seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n## 📧 Contact\n\nFor bug reports and feature requests related to YOLOv5, please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). 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