Ultralytics YOLO11 🚀
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Updated
Dec 24, 2024 - Python
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-YOLO: A high-performance, easy-to-use YOLO deployment toolkit for NVIDIA, powered by TensorRT plugins and CUDA Graph, supporting C++ and Python.
🚀 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
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.
This repository is based on shouxieai/tensorRT_Pro, with adjustments to support YOLOv8.
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.
Based on tensorrt v8.0+, deploy detection, pose, segment, tracking of YOLO11 with C++ and python api.
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.
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