Ultralytics YOLO 🚀

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

🔥🔥🔥 专注于YOLO11，YOLOv8、TYOLOv12、YOLOv10、RT-DETR、YOLOv7、YOLOv5改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

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

tensorrt for yolo series (YOLOv11,YOLOv10,YOLOv9,YOLOv8,YOLOv7,YOLOv6,YOLOX,YOLOv5), nms plugin support

🚀🚀🚀 YOLO series of PaddlePaddle implementation, PP-YOLOE+, RT-DETR, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv10, YOLO11, YOLOX, YOLOv5u, YOLOv7u, YOLOv6Lite, RTMDet and so on. 🚀🚀🚀

Python library for YOLO small object detection and instance segmentation

单缺口/多缺口滑块验证码识别，pip安装使用, 可本地/服务器部署API，点击下方链接在线体验 ⬇⬇⬇

a GUI application, which uses YOLOs (YOLOv8, YOLO11, YOLOv13) for Object Detection/Tracking, Human Pose Estimation/Tracking from images, videos or camera

Converting COCO annotation (CVAT) to annotation for YOLO-seg (instance segmentation) and YOLO-obb (oriented bounding box detection)

YOLOv11 re-implementation using PyTorch

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

Ultralytics Docs https://docs.ultralytics.com

Simple and Easy simulator YOLO Object Detection with Bird's Eye View

Flare Guard is an AI-powered system designed to instantly detect and report fires and smoke.

An offline AI-powered video analysis tool with object detection (YOLO), image captioning (BLIP), speech transcription (Whisper), audio event detection (PANNs), and AI-generated summaries (LLMs via Ollama). It ensures privacy and offline use with a user-friendly GUI.

Ultralytics Python Project Template

Deploy YOLO models to Replicate with ready-to-use Cog configurations and automated CI/CD. Includes optimized deployments for YOLO11, and reference implementations for custom model deployment.

A free and opensource yolov8, yolo11 and yolo26 all in one training tool that automates file structure and yaml files, auto labeling with SAM2, brush system for uninterupted labeling, a strong modular augmentation system where anybody can write their own filters and training. Without having to open terminal.

the python api for axengine runtime