Ultralytics YOLO 🚀

Implementation of popular deep learning networks with TensorRT network definition API

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.

🚀 Easier & Faster YOLO Deployment Toolkit for NVIDIA 🛠️

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

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

Ultralytics assets

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

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 ⬇⬇⬇

Ultralytics YOLO iOS App source code for running YOLO in your own iOS apps 🌟

Enhances construction site safety using YOLO for object detection, identifying hazards like workers without helmets or safety vests, and proximity to machinery or vehicles. HDBSCAN clusters safety cone coordinates to create monitored zones. Post-processing algorithms improve detection accuracy.

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

Samples code for world class Artificial Intelligence SoCs for computer vision applications.

Based on tensorrt v8.0+, deploy detection, pose, segment, tracking of YOLO11 with C++ and python api.

yolo master 本课程主要对yolo系列模型进行介绍，包括各版本模型的结构，进行的改进等，旨在帮助学习者们可以了解和掌握主要yolo模型的发展脉络，以期在各自的应用领域可以进一步创新并在自己的任务上达到较好的效果。

A Rust library integrated with ONNXRuntime, providing a collection of Computer Vison and Vision-Language models such as YOLO, FastVLM, and more.