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true Explore the extensive functionalities of the YOLOv5 object detection model, renowned for its speed and precision. Dive into our comprehensive guide for installation, architectural insights, use-cases, and more to unlock the full potential of YOLOv5 for your computer vision applications. ultralytics, yolov5, object detection, deep learning, pytorch, computer vision, tutorial, architecture, documentation, frameworks, real-time, model training, multicore, multithreading

Comprehensive Guide to Ultralytics YOLOv5

YOLOv5 CI YOLOv5 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

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!

Tutorials

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

Environments

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:

Status

YOLOv5 CI

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.