AIOTEC 是新一代开源边缘计算系统，B/S架构可视化配置，是创新融合 RTU 系统与视觉边缘计算系统。它支持多协议物联网设备接入，可实时采集控制数据，同时搭载深度学习框架，在边缘侧高效完成实时视觉分析，响应快且省带宽。适用于工业制造、智慧城市、智慧农业等场景。 架构主要开发语言为C++，适配arm64、amd64、x86等系统。 我们期望AIOTEC能够为使用人员提供更加高效、便捷、智能的体验。

Real-time American Sign Language (ASL) letters detection, via PyTorch, OpenCV, YOLOv5, Roboflow and LabelImg 🤟

# 🎯 YOLOv智能标注工具 一个功能强大的网页端YOLOv自动标注工具，支持目标检测和实例分割，可加载自定义训练模型，自动标注图片和视频，并导出为多种训练格式，可调整数据集导出比例，自定义类型ID。

A simple way to use cookies in Reason (OCaml) on the frontend. 🍪

Open DRUWA - Open Deep Realtime User Welcoming Assistant

Multi-Object Tracking and Trajectory Prediction. This repository contains all codes written for SUMMER RESEARCH INTERNSHIP (2021) at AI and Robotics Park (ARTPARK), IISc Bangalore

A Django based web project that uses custom YOLO model, trained on google open images, to detect and count number of fruits(Apple, Mango, Orange, Pomegranate, tomato) in an image.

👀 A web app for detecting and counting objects in videos using YOLOv8 for detection and ByteTrack for tracking, with customizable regions of interest (ROIs) function.

A Unity-based pipeline generates labeled synthetic data, converts it to YOLO format, and merges it with real images for training. Mixed synthetic-real datasets improve detection performance on underrepresented object classes by increasing controlled visual diversity.

Live time object detector. Contributions are welcomed for more diversified models

基于YOLOv8的行人检测系统

RealVision: A powerful, real-time object detection and understanding application using Python, OpenCV, and state-of-the-art AI models. Features dual model support (YOLO v8 + MobileNet-SSD), object tracking, performance monitoring, and modern GUI interface.

A repository for an automated system using object detection and machine learning techniques to diagnose wrist abnormalities in children, adolescents, and young adults, providing high accuracy and precision in diagnosis through a user-friendly mobile application.

PyTorch implementation of YOLOv12 with Scaled Dot-Product Attention (SDPA) optimized by FlashAttention for fast and efficient object detection.

This project is a web application for detecting skin cancer using object detection techniques. It leverages the YOLOv8 model to identify various types of skin cancer from uploaded images or videos, as well as through live webcam feeds.

A detection dataset containing labelled images of French terrestrial Arthropods.

A browser-based tool that lets you train custom YOLOv8 object detection models without touching the terminal. Paste your Roboflow dataset snippet, pick your hyperparameters, and hit Start — the rest is handled for you.

Real-time object detection and tracking using YOLO models with dual-source video input support

고속도로 및 로드킬 위험 지역의 영상 데이터를 바탕으로 하여 운전자에게 실시간으로 정보를 제공해주는 지도 기반 종합 페이지 설계 및 개발

Clarity: 工业视觉模型的链上审计系统