Turn any computer or edge device into a command center for your computer vision projects.

Ultralytics YOLOv8, YOLOv9, YOLOv10, YOLOv11, YOLOv12 for ROS 2

YOLO Face 🚀 in PyTorch

YoloDotNet - A C# .NET 8.0 project for Classification, Object Detection, OBB Detection, Segmentation and Pose Estimation in both images and live video streams.

🔥🔥🔥Java免费离线AI算法工具箱，支持人脸识别，活体检测，表情识别、目标检测、实例分割、行人检测、OCR文字识别、车牌识别、表格识别、ASR+TTS、机器翻译等功能，Maven引用即可使用。支持PyTorch、Tensorflow，已集成 Mtcnn、InsightFace、SeetaFace6、YOLOv8~v12、PaddleOCR(PPOCRv5)、Whisper等主流模型

🚀 A high performance real-time object detection solution using YOLO11 ⚡️ powered by ONNX-Runtime

This repository is based on shouxieai/tensorRT_Pro, with adjustments to support YOLOv8.

🔥🔥 CVPR 2025 (Nashville TN) - The 4th Anti-UAV Workshop & Challenge 🥉

yolov3, yolo12, dino, segmenations, face, pose, keypoints on deepstream

🚀 ⭐ The list of the most popular YOLO algorithms - awesome YOLO

YOLOv12 Inference Using CPP, Tensorrt, And CUDA

YOLO Object Tracking TensorRT

Short, concise yet complete implementations of various Yolo models in the style of Tinygrad, but in pure Pytorch.

The YOLOv12 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT

Real-time monitoring, object tracking, and line-crossing detection for CCTV camera streams.

UAVDB: Point-Guided Masks for UAV Detection and Segmentation

YOLOv12 Underwater Object Detection is an open-source suite for underwater object detection, built on YOLOv12. It offers an end-to-end pipeline with GPU-accelerated training, customizable data augmentations, real-time inference via Gradio, and support for model export (ONNX & PyTorch).

https://emergentai.ca/yolov12-onnxruntime-web/

Go gRPC client for YOLO-NAS, YOLOv8-YOLOv12 inference using the Triton Inference Server.

A deep learning project for detecting and classifying teeth as normal or cavity using YOLOv12m. The model is integrated into a web application for real-time dental screening. Designed to enhance early diagnosis accessibility, with future improvements planned for dataset expansion, annotation quality, and robustness to real-world conditions.