#YOLOv5

Discover cutting-edge vision AI techniques driving visual intelligence forward. Leveraging extensive research and refined practices, this project excels in object detection, image segmentation, and classification. Access detailed resources and guides while a vibrant user community aids in optimizing AI potential across various fields. Connect via GitHub for issues or join community discussions on Discord to utilize top-tier AI tools effectively.

Make-sense is a user-friendly, web-based tool designed for photo labeling in deep learning. It runs directly in browsers without installation and is compatible with various operating systems. The tool integrates advanced AI, including YOLOv5 and TensorFlow.js, for task automation and data privacy protection. It supports multiple data export formats like CSV and COCO JSON and provides thorough documentation for local and Docker setups, offering a comprehensive solution for computer vision data preparation.

The Persian License Plate Recognition System (PLPR) effectively detects and recognizes Persian license plates, leveraging YOLOv5 models and a straightforward interface. It supports real-time processing for traffic surveillance and vehicle identification. The deep learning component enhances Persian character recognition accuracy, ensuring dependable performance across various conditions. Key features include adjustable video source settings, an easy-to-use GUI, and extensive management of residents and entrances, providing a comprehensive solution for vehicle access control and monitoring.

Discover how this project leverages state-of-the-art detection technologies in multi-object tracking, offering CNN-based feature extraction and compatibility with detectors such as YOLOv3, YOLOv5, and Mask R-CNN. Experience notable advancements in MOT tracking through GPU-accelerated NMS, batch processing, and improved computational efficiency. The project includes bug fixes, code refactoring, and introduces new features for multi-tracking such as category, ID, and target mask display. It provides a streamlined framework for effective single and distributed GPU training, enabling robust performance in object tracking applications.

The toolkit efficiently facilitates the deployment of AI models such as object and face detection, along with segmentation. Compatible with ONNXRuntime, TensorRT, and MNN, it provides platform-wide flexibility and performance. Its simple syntax and minimal dependencies endear it to developers, further complemented by extensive model support. Being open-source ensures continuous community enhancements.

YOLO Magic extends the YOLOv5 framework with advanced network modules and a user-friendly web interface for enhanced visual task performance. It includes spatial pyramid modules, feature fusion structures, and new backbone networks to improve efficiency. Suitable for both beginners and experts, it streamlines image inference and model processes. The active community offers extensive resources for customization and learning. Explore YOLO Magic for top-tier object detection and analysis.

Discover methods for tracking and analyzing football players with AI. This guide explores YOLOv5 and ByteTrack for real-time player tracking, YOLOv7 for 3D pose estimation, and GPT-4V for identifying team uniforms by color. Gain insights into computer vision techniques for sports analytics.

This project combines training and inference for object detection using a dynamic shape strategy, based on the YOLOv5 model framework. It incorporates pre-processing and post-processing directly into the model graph, thereby facilitating deployment on platforms such as LibTorch, ONNX Runtime, TVM, and TensorRT. The design takes cues from Ultralytics's YOLOv5, ensuring familiarity for those used to torchvision's models. Recent enhancements include TensorRT C++ interface integration and expanded ONNX Runtime support. The project offers simple installation via PyPI or source with minimal dependencies, enhancing the efficiency of both Python and C++ deployment.