Ultralytics YOLO11 🚀

Implementation of popular deep learning networks with TensorRT network definition API

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

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

🚀 Use YOLO11 in real-time for object detection tasks, with edge performance ⚡️ powered by ONNX-Runtime.

Python library for YOLO small object detection and instance segmentation

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

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.

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

Converting COCO annotation (CVAT) to annotation for YOLOv8-seg (instance segmentation) and YOLOv8-obb (oriented bounding box detection)

A comprehensive tool for processing and analyzing video footage, producing detailed insights into gameplay and player performance enhancing game understanding and performance evaluation.

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

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

YOLOv11 re-implementation using PyTorch

Persian LicensePlate Recognition System using YOLO11 and OpenCV

This repository contains Jupyter Notebooks for training the YOLO11 model on custom datasets for image classification, instance segmentation, object detection, and pose estimation tasks.

Ultralytics VSCode snippets plugin to provide quick examples and templates of boilerplate code to accelerate your code development and learning.

the python api for axengine runtime

Explore computer vision use cases and projects across various industries, including retail, transportation, medical imaging, manufacturing, agriculture, wildlife, and more, leveraging state-of-the-art models for object detection, image segmentation, object tracking, pose estimation, and beyond.

使用opencv部署yolo11表格检测，它是百度网盘AI大赛-表格检测的第2名方案，方案里包含表格框检测，表格角点检测，表格方向分类，一共三个模块。我依然是编写了C++和Python两个版本的程序