Ultralytics YOLOv3 provides a PyTorch implementation of the YOLOv3 object detection model, optimized for speed and accuracy. It targets researchers and developers needing a robust baseline for computer vision tasks, offering straightforward integration and deployment across multiple platforms.

How It Works

This implementation leverages PyTorch, building upon the established YOLOv3 architecture. It utilizes Darknet-53 as its feature extractor and incorporates multi-scale predictions and logistic classifiers for improved accuracy, particularly with small objects and multi-label classification. The project emphasizes ease of use and exportability to formats like ONNX, CoreML, and TFLite.

Quick Start & Requirements

• Install via pip: pip install ultralytics
• Clone repo and install dependencies: git clone https://github.com/ultralytics/yolov3, cd yolov3, pip install -r requirements.txt
• Requires Python >= 3.8.0 and PyTorch >= 1.8.
• Inference can be performed using PyTorch Hub or the detect.py script.
• Training examples are provided for COCO dataset.
• Official Docs: https://docs.ultralytics.com/

Highlighted Details

• Supports inference via PyTorch Hub and detect.py script with various input sources (images, videos, streams, webcam).
• Offers training scripts for YOLOv3, YOLOv3-SPP, and YOLOv3-tiny on datasets like COCO.
• Includes tutorials for custom data training, model export (ONNX, TFLite, CoreML), and deployment on NVIDIA Jetson.
• Integrates with experiment tracking tools like Weights & Biases and Comet ML.

Maintenance & Community

• Active development, with mentions of newer models like YOLO11.
• Community support via Discord: https://discord.com/invite/ultralytics
• GitHub Issues for support: https://github.com/ultralytics/yolov5/issues/new/choose

Licensing & Compatibility

• AGPL-3.0 License: Suitable for academic and personal projects, requires derivative works to be open-sourced.
• Enterprise License available for commercial use.

Limitations & Caveats

• While the repo is named yolov3, the README indicates it's based on YOLOv5 structure, suggesting potential compatibility nuances for pure YOLOv3 users.
• YOLOv3-specific documentation may be limited, directing users to general YOLO principles.