# Real-time Object Detection

A lightweight face detection model based on the YOLO architecture, specifically designed for efficient face detection, trained on the WIDERFACE dataset.

This model is an object detection model fine-tuned on the medical protective equipment dataset CPPE-5, based on PekingU/rtdetr_r50vd_coco_o365

RT-DETRv2 is a real-time object detection model based on the Transformer architecture, enhanced by an improved baseline model and free optimization tricks.

YOLOv9 model converted to CoreML format, capable of running efficiently on Apple Neural Engine for object detection tasks.

YOLOv8 model converted to CoreML format, capable of running on Apple Neural Engine

YOLOv10x is the latest version of the YOLO series, focusing on real-time end-to-end object detection, offering higher detection accuracy and faster inference speed.

YOLOv10 is a real-time end-to-end object detection model developed by the Tsinghua University team, based on the latest improved version of the YOLO series.

YOLOv10 is a real-time end-to-end object detection model developed by the Tsinghua University team, representing the latest improvement in the YOLO series.

YOLOv10 is a real-time end-to-end object detection model proposed by Tsinghua University, known for its efficiency and accuracy.

YOLOv10 is a real-time end-to-end object detection model proposed by Tsinghua University, known for its efficiency and accuracy.

RT-DETR is the first real-time end-to-end object detector, achieving a balance of speed and accuracy surpassing the YOLO series through efficient hybrid encoders and query selection mechanisms

YOLOv10 is a real-time object detection model that achieves efficient and overhead-free object detection by eliminating post-processing steps such as Non-Maximum Suppression (NMS).

YOLOv10 is a real-time end-to-end object detection model that offers a balance between efficient detection performance and accuracy.

RT-DETR is the first real-time end-to-end object detection Transformer model, achieving efficient NMS-free detection through a hybrid encoder and query selection mechanism

RT-DETR is the first real-time end-to-end object detector, achieving 53.1% AP and 108 FPS on the COCO dataset through an efficient hybrid encoder and uncertainty-minimized query selection mechanism.

Object detection model based on YOLOv9, adapted for Transformers.js, capable of running in a browser

A lightweight real-time multi-object detection model optimized for Unity engine, based on YOLOv7-tiny architecture

A fine-tuned hard hat detection model based on YOLOS-tiny, designed for object detection tasks in workplace safety monitoring scenarios.

This project integrates the YOLO object detection model with the LLaMa 2 7B large language model, aiming to provide navigation assistance for visually impaired individuals in their daily travels.

An object detection model based on YOLOv8m, specifically designed for detecting protective equipment and its absence.

An object detection model based on YOLOv8s, specifically designed for detecting protective equipment such as gloves, goggles, helmets, etc.

An object detection model based on YOLOv8n, specifically designed for detecting the use of personal protective equipment.

Aircraft object detection model based on YOLOv8s, optimized for aircraft recognition tasks.