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Comprehensive Guide to Ultralytics YOLOv5

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Welcome to the Ultralytics' YOLOv5🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.

Built on PyTorch, this powerful deep learning framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your computer vision projects. Let's get started!


Here's a compilation of comprehensive tutorials that will guide you through different aspects of YOLOv5.


YOLOv5 is designed to be run in the following up-to-date, verified environments, with all dependencies (including CUDA/CUDNN, Python, and PyTorch) pre-installed:



This badge signifies that all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify the correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and with every new commit.

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Created 2023-11-12, Updated 2023-11-27
Authors: glenn-jocher (3), sergiuwaxmann (1), ouphi (1)