X-AnyLabeling README

The project's README from github.com/CVHub520/X-AnyLabeling. As one markdown file: README.md.

X-AnyLabeling

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YOLO Vision 2026

X-AnyLabeling interface

🥳 What's New

Introduction

X-AnyLabeling is a lightweight, efficient, and unified cross-platform desktop application for AI-assisted annotation of text, image, video, 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

Model library

Task Category Supported Models
🖼️ Image Classification YOLOv5-Cls, YOLOv8-Cls, YOLO11-Cls, InternImage, PULC
🎯 Object Detection YOLOv5/6/7/8/9/10, YOLO11/12/26, YOLOX, YOLO-NAS, D-FINE, DAMO-YOLO, Gold_YOLO, RT-DETR, RF-DETR, DEIMv2
🖌️ Instance Segmentation YOLOv5-Seg, YOLOv8-Seg, YOLO11-Seg, YOLO26-Seg, Hyper-YOLO-Seg, RF-DETR-Seg, D-FINE-seg
🏃 Pose Estimation YOLOv8-Pose, YOLO11-Pose, YOLO26-Pose, DWPose, RTMO
😀 Face Estimation SCRFD, YOLOv6Lite-Face
👣 Tracking TrackTrack, Bot-SORT, ByteTrack, SAM2/3-Video
🔄 Rotated Object Detection YOLOv5-Obb, YOLOv8-Obb, YOLO11-Obb, YOLO26-Obb, RT-DETRv2-OBB
📏 Depth Estimation Depth Anything
🧩 Segment Anything SAM 1/2/3, SAM-HQ, SAM-Med2D, EdgeSAM, EfficientViT-SAM, MobileSAM
✂️ Image Matting RMBG 1.4/2.0
💡 Proposal UPN
🏷️ Tagging RAM, RAM++
📄 OCR PP-OCRv4, PP-OCRv5, PP-OCRv6
🧾 Layout Analysis PP-DocLayoutV3
📑 Document Parsing PaddleOCR-VL, PaddleOCR-VL-1.6
🗣️ Vision Foundation Models Rex-Omni, Florence2
👁️ Vision Language Models Qwen3-VL, Gemini, ChatGPT, GLM
🛣️ Lane Detection CLRNet
🔢 Object Counting CountGD, GeCO, GeCo2
📍 Grounding Grounding DINO, YOLO-World, YOLOE, SAM 3, LocateAnything
📚 Other 👉 model_zoo 👈

Docs

  1. Remote Inference Service
  2. Installation & Quickstart
  3. Usage
  4. Command Line Interface
  5. Customize a model
  6. Chatbot
  7. VQA
  8. Image Classifier
  9. Video Classifier
  10. Document Parsing and Intelligent Text Recognition

Examples

Contribute

We believe in open collaboration! X‑AnyLabeling continues to grow with the support of the community. Whether you're fixing bugs, improving documentation, or adding new features, your contributions make a real impact.

To get started, please read our Contributing Guide and make sure to agree to the Contributor License Agreement (CLA) before submitting a pull request.

If you find this project helpful, please consider giving it a ⭐️ star! Have questions or suggestions? Open an issue or email us at [email protected].

A huge thank you 🙏 to everyone helping to make X‑AnyLabeling better.

License

This project is licensed under the GNU General Public License v3.0. You may use, modify, and redistribute the software, including for commercial purposes, provided that you comply with the terms of the license.

Sponsor

X-AnyLabeling is an actively maintained open-source project. Your sponsorship helps support feature development, model integration, documentation, and community support.

Sponsor the X-AnyLabeling project

Click the image above to visit the sponsorship page.

Acknowledgement

I extend my heartfelt thanks to the developers and contributors of AnyLabeling, LabelMe, LabelImg, roLabelImg, PPOCRLabel and CVAT, whose work has been crucial to the success of this project.

Citing

If you use this software in your research, please cite it as below:

@misc{X-AnyLabeling,
  year = {2023},
  author = {Wei Wang},
  publisher = {Github},
  organization = {CVHub},
  journal = {Github repository},
  title = {X-AnyLabeling: A Unified Desktop Platform for AI-Assisted Data Annotation},
  howpublished = {\url{https://github.com/CVHub520/X-AnyLabeling}}
}
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X-AnyLabeling (CVHub520/X-AnyLabeling)

Searching Database…

这是什么?一款免费的跨平台桌面应用,用于 AI 辅助标注文本、图像、视频和多模态数据。既提供手动标注工具,也内置深度学习模型,可自动预标注或批量标注,并支持多种格式导出。
它是桌面应用还是在浏览器里运行?它是原生桌面应用,不是网页服务——可安装在 Windows、Linux 和 macOS 上,界面支持英语、简体中文、日语和韩语。
我怎么把它跑起来?按照 README 文档(Docs)部分链接的安装与快速入门指南操作——你需要为所用操作系统安装桌面版。另外还提供命令行界面的文档,方便用脚本自动化处理。
用 AI 辅助标注需要很强的 GPU 或付费的 API 密钥吗?核心模型通过 ONNX Runtime、TensorRT 或 OpenCV DNN 在本地运行,不需要任何 API 密钥。你也可以选择连接云端的视觉语言模型(Gemini、ChatGPT、GLM),或者把任务转发到远程的 X-AnyLabeling-Server。
我的数据集会留在自己电脑上,还是会被上传到别处?默认留在本地——推理就在你自己的硬件上进行。只有当你主动选择通过 X-AnyLabeling-Server 或云端模型后端进行远程推理时,数据才会离开电脑。
这个许可证可以用于商业标注工作吗?它采用 GPLv3——只要遵守许可证条款,你可以使用、修改和再分发该应用,包括用于商业用途。
它支持哪些标注任务?覆盖范围很广:图像分类、普通框和旋转框的目标检测、实例分割和语义分割、姿态估计和人脸估计、多目标跟踪、OCR 和关键信息提取、车道线标注、深度估计、图像抠图、打标签、图像描述、视觉问答以及文档解析。绘图工具包括多边形、矩形、旋转框、长方体、圆形、直线、折线、点和掩码。
它自带哪些 AI 模型?涵盖各类任务的几十个模型:从 v5 到 YOLO26 的 YOLO 系列,用于检测、分割和姿态估计;RT-DETR、RF-DETR、D-FINE 等 DETR 类模型;SAM 1/2/3 以及 MobileSAM、EfficientViT-SAM 等轻量版本;用于文本提示检测的 Grounding DINO 和 YOLO-World;用于 OCR 和文档解析的 PP-OCRv4/v5/v6 和 PaddleOCR-VL;Depth Anything;还有 Qwen3-VL、Florence2、Gemini、ChatGPT 和 GLM 等视觉语言模型。
我可以导入和导出哪些数据集格式?它支持读写 COCO、VOC、YOLO、DOTA、MOT、MASK、PPOCR、MMGD、VLM-R1 和 ShareGPT 格式,标注结果可以直接接入常见的训练流程。
我能接入自己的模型吗?可以——应用支持自定义模型集成、灵活扩展和二次开发,并提供了专门的“自定义模型”指南。如果你要部署自己的后端,配套的 X-AnyLabeling-Server 可以把自定义模型和计算资源连接起来,进行远程推理。
Creator
CVHub520
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Commits · 90 Days