Search Packages

ultralytics

Ultralytics YOLO 🚀

ultralytics-thop

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

yolov5

Packaged version of ultralytics/yolov5 + many extra features

ultralytics-opencv-headless

Ultralytics YOLO 🚀

mkdocs-ultralytics-plugin

MkDocs plugin for Ultralytics Docs at https://docs.ultralytics.com

ultralytics-actions

Ultralytics GitHub Actions

openadapt

Open Source Generative Process Automation (i.e. Generative RPA). AI-First Process Automation with Large ([Language (LLMs) / Action (LAMs) / Multimodal (LMMs)] / Visual Language (VLMs)) Models

fovux-mcp

Local-first YOLO workbench for edge-AI computer vision: MCP server + VS Code Studio for dataset inspection, training, evaluation, export, and RTSP inference.

ultralytics-dev

Ultralytics YOLO 🚀

yolo-scout

YOLO Dataset Quality Analysis Tool powered by FiftyOne

modeyolo

ModeYOLO is a versatile Python package designed for efficient color space transformations and simplified dataset modification for deep learning applications. Seamlessly integrating into your workflow, this package empowers users to effortlessly perform diverse color operations and streamline the creation of modified datasets, enhancing the flexibility and convenience of machine learning model training processes.

ultralytics-yolov5

No description available

zmkj-rknn-yolov8

自用rknn yolov8 版本 只修复了 weights_only=False 问题 来源https://github.com/airockchip/ultralytics_yolov8 8.0.151 v1.6分支

tensorrt-yolo

🚀 Easier & Faster YOLO Deployment Toolkit for NVIDIA 🛠️

asone

Easy & Modular Computer Vision Detectors, Trackers & SAM - Run YOLOv9,v8,v7,v6,v5,R,X in under 10 lines of code.

visiofirm

Fast almost fully automated image annotation tool for computer vision tasks detection, oriented bounding boxes and segmentation.

trtyolo-export

🚀 Easier & Faster YOLO Deployment Toolkit for NVIDIA 🛠️

yolov5-inference

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

waffle-hub

Waffle hub

labelme-to-yolo

Python library to convert LabelMe JSON annotations into YOLO (YOLOv5/YOLOv7) format, including automatic dataset splitting and structure generation for computer vision workflows.

ymsyolo10

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

syml-ultralytics

Ultralytics YOLO 🚀

ultralytics-rknn

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

ultralytics-yolov10-rknn

Ultralytics YOLO 🚀