Ultralytics YOLOv5, a PyTorch-based computer vision toolkit for object detection, instance segmentation and image classification with training and export.
58.2K17.5KPythonAGPL-3.0Updated 2 days ago
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What is it?
X-AnyLabeling is a desktop application for annotating datasets used in computer vision and multimodal AI. It covers 2D tasks such as classification, object detection, instance segmentation, pose estimation, oriented object detection and OCR, along with multi-object tracking, lane annotation, image captioning, visual question answering, document parsing and 3D point cloud detection and segmentation.
The app ships with drawing tools for polygons, rectangles, rotated boxes, cuboids, points, masks and more, plus a large library of deep learning models such as YOLO variants, SAM, Grounding DINO and PaddleOCR that generate labels automatically. Inference can run locally through ONNX Runtime, TensorRT, OpenCV DNN, or remotely through vLLM and SGLang, and the project includes a companion X-AnyLabeling-Server for hosting custom models.
Annotations can be imported and exported in formats including COCO, VOC, YOLO, DOTA, MOT, MASK, PPOCR and ShareGPT. The application runs on Windows, Linux and macOS, and its interface is available in English, Simplified Chinese, Japanese and Korean.
Key features
- 01Built-in drawing tools for polygons, rectangles, rotated boxes, cuboids, points and masks.
- 02Automated labeling with many detection, segmentation, OCR and vision-language models.
- 03Local inference via ONNX Runtime, TensorRT and OpenCV DNN, or remote inference via vLLM and SGLang.
- 04Import and export in COCO, VOC, YOLO, DOTA, MOT, PPOCR and ShareGPT formats.
- 053D point cloud detection and segmentation annotation.
- 06Runs on Windows, Linux and macOS.
Use cases
- Labeling image datasets for object detection or instance segmentation training.
- Pre-labeling data with SAM or YOLO models and correcting results by hand.
- Annotating documents and text for OCR or key information extraction.
- Preparing 3D point cloud data for autonomous driving or robotics perception tasks.
Quick start
- 1
Open the installation and quickstart guide from the README
- 2
Download the app from the releases page or install it following the guide
- 3
Launch X-AnyLabeling and start annotating
Things to know
- Licensed under GPL-3.0, a copyleft license that requires derivative distributions to be open source under the same terms.
- Requires Python 3.11 or newer, per the badge in the README.
Topics
FAQ
- What is X-AnyLabeling?
- A cross-platform desktop app for AI-assisted annotation of text, image, video, point cloud and multimodal data, with built-in models and multi-format export. X-AnyLabeling is a desktop application for annotating datasets used in computer vision and multimodal AI. It covers 2D tasks such as classification, object detection, instance segmentation, pose estimation, oriented object detection and OCR, along with multi-object tracking, lane annotation, image captioning, visual question answering, document parsing and 3D point cloud detection and segmentation.
- Is X-AnyLabeling free to use? What license does it have?
- X-AnyLabeling is open source under the GPL-3.0 license. Check the license terms before using it in commercial products.
- Is X-AnyLabeling still maintained?
- Actively maintained. The last commit to X-AnyLabeling was on Oct 18, 2026.
- What can I use X-AnyLabeling for?
- Labeling image datasets for object detection or instance segmentation training. Pre-labeling data with SAM or YOLO models and correcting results by hand. Annotating documents and text for OCR or key information extraction. Preparing 3D point cloud data for autonomous driving or robotics perception tasks.
- How do I install X-AnyLabeling?
- Open the installation and quickstart guide from the README Download the app from the releases page or install it following the guide Launch X-AnyLabeling and start annotating
- Where can I get X-AnyLabeling?
- The source code and installation instructions are on GitHub: https://github.com/CVHub520/X-AnyLabeling
Summary generated by AI from the project's README. GitHub stats refresh daily.
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