lyuwenyu/RT-DETR

YOLOv10 is a PyTorch computer vision library and real-time vision framework designed for locating and identifying multiple objects in images and video streams. It functions as an end-to-end object detector that optimizes for high-speed deployment and detection precision. The project is distinguished by an NMS-free detection architecture that predicts a single bounding box per object, eliminating the need for non-maximum suppression post-processing to reduce inference latency. It further optimizes for edge hardware through scalable weights and a quantization-friendly structure that facilitates

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra

This project is a PyTorch implementation of the EfficientDet architecture designed for real-time object detection. It provides a neural network and inference engine capable of identifying and locating multiple objects within images or video streams. The implementation includes pretrained computer vision models with optimized weights, enabling immediate inference and fine-tuning without the need for training from scratch. The project covers the full pipeline for computer vision model optimization, including custom object detection training and model weight optimization. It incorporates struct

Deformable-DETR is an object detection system for computer vision that uses a transformer-based encoder-decoder architecture. It identifies and locates objects within images by representing potential targets as a set of learnable queries. The project employs sampling-based attention to restrict attention to a small set of points around a reference, reducing computational complexity and speeding up convergence. It further utilizes multi-scale feature fusion to detect objects of varying sizes within a single frame. The system includes capabilities for training models across multiple GPU cluste