Advanced Auto Labeling Solution with Added Features
Project description
Auto-Labeling
Text/Visual Prompting and Prompt-free for Detection & Segmentation
Detect Anything
Segment Anything
Chatbot
VQA
Image Classifier
🥳 What's New
- Bump version to
3.3.0-rc.1 - Add support for real-time instance segmentation model based on RF-DETR-Seg (#1184)
- Add support for remote server inference via X-AnyLabeling-Server (#1175)
- Add CLI support for annotation conversion tasks (#980)
- Add Shape Manager for batch operations on video frame sequences (thanks @ltnetcase) (#1128)
- Add shape visibility control in Label Manager for showing/hiding labels on canvas (#1172)
- Add multi-label classification mode to Image Classifier
- For more details, please refer to the CHANGELOG
X-AnyLabeling
X-AnyLabeling is a powerful annotation tool that integrates an AI engine for fast and automatic labeling. It's designed for multi-modal data engineers, offering industrial-grade solutions for complex tasks.
Features
- Supports remote inference service.
- Processes both
imagesandvideos. - Accelerates inference with
GPUsupport. - Allows custom models and secondary development.
- Supports one-click inference for all images in the current task.
- Enable import/export for formats like COCO, VOC, YOLO, DOTA, MOT, MASK, PPOCR, MMGD, VLM-R1.
- Handles tasks like
classification,detection,segmentation,caption,rotation,tracking,estimation,ocrand so on. - Supports diverse annotation styles:
polygons,rectangles,rotated boxes,circles,lines,points, and annotations fortext detection,recognition, andKIE.
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, YOLOX, YOLO-NAS, D-FINE, DAMO-YOLO, Gold_YOLO, RT-DETR, RF-DETR, DEIMv2 |
| 🖌️ Instance Segmentation | YOLOv5-Seg, YOLOv8-Seg, YOLO11-Seg, Hyper-YOLO-Seg, RF-DETR-Seg |
| 🏃 Pose Estimation | YOLOv8-Pose, YOLO11-Pose, DWPose, RTMO |
| 👣 Tracking | Bot-SORT, ByteTrack |
| 🔄 Rotated Object Detection | YOLOv5-Obb, YOLOv8-Obb, YOLO11-Obb |
| 📏 Depth Estimation | Depth Anything |
| 🧩 Segment Anything | SAM, 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 |
| 🗣️ Vision Foundation Models | Florence2 |
| 👁️ Vision Language Models | Qwen3-VL, Gemini, ChatGPT |
| 🛣️ Land Detection | CLRNet |
| 📍 Grounding | CountGD, GeCO, Grounding DINO, YOLO-World, YOLOE |
| 📚 Other | 👉 model_zoo 👈 |
Docs
- Remote Inference Service
- Installation & Quickstart
- Usage
- Customize a model
- Chatbot
- VQA
- Multi-class Image Classifier
Examples
- Classification
- Detection
- Segmentation
- Description
- Estimation
- OCR
- MOT
- iVOS
- Matting
- Vision-Language
- Counting
- Training
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 cv_hub@163.com.
A huge thank you 🙏 to everyone helping to make X‑AnyLabeling better.
License
This project is licensed under the GPL-3.0 license and is only free to use for personal non-commercial purposes. For academic, research, or educational use, it is also free but requires registration via this form here. If you intend to use this project for commercial purposes or within a company, please contact cv_hub@163.com to obtain a commercial license.
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 = {Advanced Auto Labeling Solution with Added Features},
howpublished = {\url{https://github.com/CVHub520/X-AnyLabeling}}
}
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