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Glenn Jocher edited this page Oct 22, 2024 · 44 revisions

Welcome to the Ultralytics YOLO wiki! 🎯 Here, you'll find all the resources you need to get the most out of the YOLO object detection framework. From in-depth tutorials to seamless deployment guides, explore the powerful capabilities of YOLO for your computer vision needs. For full documentation, head to Ultralytics Docs.

Tutorials

Unlock YOLO's full potential with our step-by-step tutorials! Whether you're training a model, validating performance, or deploying it in real-world applications, we’ve got you covered. These guides will elevate your computer vision skills and help you leverage YOLO for superior results.

  • Quickstart Guide: Get YOLO up and running in just a few easy steps.

  • Operation Modes: Learn how to operate YOLO in various modes for different use cases.

    • Train: Train YOLO on custom datasets with precision.
    • Validate: Validate your trained model's accuracy and performance.
    • Predict: Detect objects and make predictions using YOLO.
    • Export: Export models to different formats for diverse environments.
    • Track: Track objects across video sequences.
    • Benchmark: Benchmark your YOLO model's performance across multiple formats.
  • Tasks: Explore the range of tasks YOLO can tackle effortlessly.

Environments

Run YOLO seamlessly in any verified environment, fully equipped with dependencies like CUDA, CUDNN, Python, and PyTorch. YOLO's flexibility ensures compatibility with the latest technology stacks.

Status

Ultralytics CI

When this badge is green, all Ultralytics CI tests are passing! Our CI process runs tests on macOS, Windows, and Ubuntu every 24 hours and on every commit. These tests ensure the seamless performance of YOLO's Modes and Tasks across various environments, so you can always count on YOLO to deliver top-notch results. 🔧


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