X-AnyLabeling: an industrial-grade auto-labeling tool for multi-modal data with integrated AI inference

What it solves

X-AnyLabeling is an industrial-grade annotation tool designed to eliminate the tedious manual effort of labeling multi-modal data. It integrates an AI engine to provide fast, automatic labeling for images and videos, significantly reducing the time required to prepare datasets for machine learning.

How it works

The tool provides a graphical user interface (GUI) that allows users to draw shapes (like polygons, rectangles, and cuboids) or use AI models to automatically generate labels. It supports multiple inference backends (ONNX Runtime, TensorRT, and OpenCV DNN) and can connect to remote inference services via X-AnyLabeling-Server. It integrates a vast library of pre-trained models for various computer vision tasks, including object detection, segmentation, and OCR.

Who it’s for

It is primarily built for multi-modal data engineers and researchers who need to create high-quality labeled datasets for training AI models in fields like computer vision and document parsing.

Highlights

• Comprehensive Model Library: Supports a wide array of models including YOLO series, SAM (Segment Anything Model), and various Vision-Language Models (VLMs) like Qwen3-VL and Gemini.
• Multi-modal Support: Handles images and videos, supporting tasks from simple classification to complex 3D cuboid annotation and multi-object tracking.
• Flexible Export Formats: Supports a variety of industry-standard formats such as COCO, VOC, YOLO, DOTA, and ShareGPT.
• AI-Assisted Workflow: Features one-click inference for all images in a task, auto-training, and interactive grounding for open-vocabulary labeling.