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true Deep dive into Ultralytics' YOLOv5. Learn about object detection model - YOLOv5, how to train it on custom data, multi-GPU training and more. YOLOv5, object detection, computer vision, CUDA, PyTorch tutorial, multi-GPU training, custom dataset, model export, deployment, CI tests

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!

Explore and Learn

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

Supported Environments

Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as CUDA, CUDNN, Python, and PyTorch, to kickstart your projects.

Project Status

YOLOv5 CI

This badge indicates that all YOLOv5 GitHub Actions Continuous Integration (CI) tests are successfully passing. These CI tests rigorously check the functionality and performance of YOLOv5 across various key aspects: training, validation, inference, export, and benchmarks. They ensure consistent and reliable operation on macOS, Windows, and Ubuntu, with tests conducted every 24 hours and upon each new commit.


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Connect and Contribute

Your journey with YOLOv5 doesn't have to be a solitary one. Join our vibrant community on GitHub, connect with professionals on LinkedIn, share your results on Twitter, and find educational resources on YouTube. Follow us on TikTok and Instagram for more engaging content.

Interested in contributing? We welcome contributions of all forms; from code improvements and bug reports to documentation updates. Check out our contributing guidelines for more information.

We're excited to see the innovative ways you'll use YOLOv5. Dive in, experiment, and revolutionize your computer vision projects! 🚀