X-AnyLabeling
X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, point cloud and multimodal data, comb
What is X-AnyLabeling?
X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, point cloud and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.
Overview of X-AnyLabeling
What it is
X-AnyLabeling is a lightweight, efficient, and unified cross-platform desktop application for AI-assisted annotation of text, image, video, point cloud, and multimodal data. It combines versatile built-in tools, automated labeling workflows, state-of-the-art deep learning models, and flexible multi-format import and export. For remote inference, X-AnyLabeling-Server provides a lightweight, extensible backend for connecting custom models and compute resources.
Key features
- Unified support for annotating and processing text, image, video, point cloud, and multimodal data.
- Covers tasks such as 2D image classification, object detection, instance segmentation, pose estimation, oriented object detection, multi-object tracking, optical character recognition, lane annotation, image captioning, visual question answering, document parsing, and 3D point clouds.
- Provides polygons, rectangles, cuboids, rotated boxes, quadrilaterals, circles, lines, polylines, points, masks, and task-specific tools for text detection, text recognition, and KIE.
- Integrates a wide range of state-of-the-art deep learning models for AI-assisted annotation, automated labeling, and batch dataset prediction.
- Supports both local and remote inference through engines and serving frameworks such as ONNX Runtime, TensorRT, OpenCV DNN, vLLM, and SGLang.
- Supports importing and exporting formats such as COCO, VOC, YOLO, DOTA, MOT, MASK, PPOCR, MMGD, VLM-R1, and ShareGPT.
- Runs on Windows, Linux, and macOS, with interfaces available in English, Simplified Chinese, Japanese, and Korean, and supports custom model integration, flexible extension, and secondary development.
Use cases
Use cases include creating labeled datasets for object detection, instance segmentation, pose estimation, OCR, lane detection, image captioning, visual question answering, document parsing, and 3D point cloud analysis. The application is suitable for preparing training data for AI models in various domains such as autonomous driving, medical imaging, document analysis, and more.
Getting started
See the repository README for installation and quickstart instructions.
License and pricing
Licensed under the GNU General Public License v3.0. Open source and free to use.
Based on the project's public README and repository metadata.
