X-AnyLabeling is a powerful annotation tool integrated with an AI engine for fast and automatic labeling. Designed for multi-modal data engineers, it offers industrial-grade solutions for complex tasks. Supports images and videos, GPU acceleration, custom models, one-click inference for all task images, and import/export formats like COCO, VOC, YOLO. Handles classification, detection, segmentation, captioning, rotation, tracking, estimation, OCR, VQA, grounding, etc., with various annotation styles including polygons, rectangles, rotated boxes.
X-AnyLabeling is an open-source annotation platform that revolutionizes data labeling workflows by incorporating cutting-edge AI models for effortless and efficient annotation. Developed as an extension and enhancement of traditional tools, it targets multi-modal data processing, particularly in computer vision tasks, making it indispensable for AI researchers, developers, and enterprises involved in machine learning pipelines.
At its heart, X-AnyLabeling simplifies the labor-intensive process of data annotation by leveraging AI assistance. Users can handle both static images and dynamic videos, benefiting from GPU-accelerated inference to speed up processing. The tool supports a wide array of import and export formats, including COCO, VOC, YOLO, DOTA, MOT, MASK, PPOCR, MMGD, and VLM-R1, ensuring compatibility with popular datasets and frameworks.
Key task categories include:
Annotation styles are diverse, covering polygons, rectangles, rotated boxes, circles, lines, points, and specialized annotations for text detection, recognition, and KIE.
What sets X-AnyLabeling apart is its seamless integration of state-of-the-art models into an intuitive interface. The Auto-Labeling and Auto-Training capabilities allow for one-click inference across entire datasets, drastically reducing manual effort. For instance, the Detect Anything mode uses grounding models to identify objects without predefined classes, while Promptable Concept Grounding enables precise localization via natural language prompts.
The tool also includes a Chatbot for interactive queries and an Image Classifier for multi-class predictions. Remote inference is facilitated through the companion X-AnyLabeling-Server, a lightweight framework for distributed processing.
Recent updates include support for Segment Anything 3 (SAM3) with enhanced prompting, TinyObj mode for improved small-object handling, and expanded model zoo documentation.
Installation is straightforward, supporting Python 3.10+ on Linux, Windows, and macOS. Users can quickly start via pip or from source, with comprehensive docs covering quickstart, user guides, CLI, custom model integration, chatbot, VQA, and classifiers.
For developers, secondary development is encouraged: customize models, add features, or integrate new tasks. Examples abound for classification, detection, segmentation, OCR, MOT, matting, vision-language tasks, counting, grounding, and training with Ultralytics.
Released under LGPL v3 (noted as GPL-3.0 in some docs), it's fully open-source and free for commercial use, with requirements to retain branding and source attribution. The project has garnered over 7,300 stars on GitHub, reflecting its community impact. Contributions are welcome via pull requests, following the CLA. For usage statistics, a voluntary registration form is available.
Developed by individual contributor Wei Wang under CVHub, it's inspired by tools like AnyLabeling, LabelMe, LabelImg, and CVAT. Sponsorships via Ko-fi, WeChat, or Alipay help sustain development.
In summary, X-AnyLabeling bridges the gap between AI innovation and practical annotation needs, empowering users to build high-quality datasets efficiently.
