NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

This is intended for streaming FOMO(object detection model) trained using Edge impulse results from Esp32-S3 to webserver

C++ object detection inference from video or image input source

Smart Traffic Flow Management System

NVR with realtime local object detection for IP cameras

Video and Image Analytics for Multiple Environments

Object Detection in OBS, real-time, local, GPU optional

Implementation of yolo v10 in c++ std 17 over opencv and onnxruntime

定时获取谷歌学术和arxiv论文的相关更新 (代码只有一个py文件，较简单有注释)

A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.

Profile PyTorch models for FLOPs and parameters, helping to evaluate computational efficiency and memory usage.

Fast and Accurate ML in 3 Lines of Code

⛅ Versatile Data Pipeline (VDP) console website

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

NetworkOptix open-source components used to build Powered-by-Nx products including Desktop Client for Network Optix Video Management Platform.

autoupdate paper list

The open-source tool for building high-quality datasets and computer vision models

A Raspberry Pi camera for taking pictures using object detection.

The "you only glance once" object detection model

This project uses YOLO models for efficient object detection with a Streamlit interface. Users can upload images or video streams for real-time detection. It supports YOLOv7, YOLOv8, and YOLOv9, offering flexibility and high accuracy in various scenarios.