This repository is an extensive, curated list of YOLO object detection projects, datasets, and related resources, targeting researchers, developers, and practitioners in computer vision. It aims to consolidate the vast YOLO ecosystem, providing a single point of reference for exploring different YOLO versions, implementations, deployment frameworks, and application domains.

How It Works

The collection is organized into a hierarchical structure, categorizing projects by YOLO version (v1-v12, YOLOX, YOLOR, etc.), implementation language (PyTorch, C++, TensorFlow, etc.), deployment targets (ONNX, TensorRT, OpenVINO, mobile), and specific application areas (autonomous driving, medical imaging, security). It also includes sections on datasets, annotation tools, and learning resources, offering a comprehensive overview of the YOLO landscape.

Quick Start & Requirements

This is a curated list, not a runnable project. To use any of the listed projects, refer to their individual repositories for installation and usage instructions.

Highlighted Details

• Comprehensive YOLO Coverage: Encompasses a wide range of YOLO versions from the original YOLOv1 to the latest YOLOv12, including popular variants like YOLOX, YOLOv7, and YOLOv8.
• Multi-Framework Implementations: Features implementations across various deep learning frameworks such as PyTorch, TensorFlow, Keras, Caffe, and MXNet.
• Extensive Deployment Options: Details projects for deployment on diverse platforms including ONNX, TensorRT, OpenVINO, NCNN, mobile (Android/iOS), and edge devices (FPGA, NPU).
• Broad Application Spectrum: Covers numerous application domains, from autonomous driving and industrial defect detection to medical imaging and security monitoring.

Maintenance & Community

The repository is maintained by the community, with contributions from numerous individuals and organizations. It serves as a community-driven resource, with links to various blogs, videos, and social media channels for further engagement.

Licensing & Compatibility

The licensing varies across the individual projects listed. Users must consult the specific licenses of each repository for compatibility and usage restrictions.

Limitations & Caveats

As a curated list, the quality and maintenance status of individual projects can vary. Users should exercise due diligence when selecting and integrating any specific YOLO implementation or resource.