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🦀 Ultralytics YOLO Rust Inference
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Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, instance segmentation, semantic segmentation, depth estimation, image classification, pose estimation, and oriented bounding box tasks.
This is a high-performance YOLO inference library written in Rust, giving those models a fast, safe, and efficient interface on ONNX Runtime. It runs every task above over images, video files, and webcam or RTSP streams, with an API designed to match the Ultralytics Python package and no Python or PyTorch runtime required.
Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!
Request an Enterprise License for commercial use at Ultralytics Licensing.
✨ Features
- 🚀 High Performance - Pure Rust implementation with zero-cost abstractions
- 🎯 Ultralytics API Compatible - Results, Boxes, Masks, Keypoints, Probs, Obb, SemanticMask, and DepthMap types matching the Python API shape
- 🔧 Multiple Backends - CPU, XNNPACK, CUDA, TensorRT, CoreML, OpenVINO, and more via ONNX Runtime
- 📦 Dual Use - Library for Rust projects + standalone CLI application
- 🏷️ Auto Metadata - Automatically reads class names, task type, and input size from ONNX models
- ⬇️ Auto Download - Downloads supported YOLO26, YOLO11, and YOLOv8 ONNX models (sizes: n/s/m/l/x) when not found locally
- 🖼️ Multiple Sources - Images, directories, glob patterns, video files, webcams, and streams
- 🪶 Lean Runtime - No PyTorch, TensorFlow, or Python runtime required
✨ Models
<a href="https://docs.ultralytics.com/tasks" target="blank" <img width="100%" src="https://cdn.ul.run/i/c99d914c3958d0755b5a3d7204b6f24a.avif" alt="Ultralytics YOLO supported tasks" </a <br <br