Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
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Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
We write your reusable computer vision tools. 💜
OpenMMLab Detection Toolbox and Benchmark
State-of-the-art 2D and 3D Face Analysis Project
deep learning for image processing including classification and object-detection etc.
Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fast and Accurate ML in 3 Lines of Code
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
OpenMMLab's next-generation platform for general 3D object detection.
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
Accelerated deep learning R&D
An open source library and framework for deep learning on satellite and aerial imagery.
World's first general purpose 3D object detection codebse.
Build fully-functioning computer vision models with PyTorch
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
基于 PatchCore 的工业异常检测项目，研究 layer2/layer3 分数融合、敏感性引导收缩与风险受控门控，并提供 Streamlit 工程化 Demo。