CVHub520/X-AnyLabeling

🥳 What's New

• 2026-11-03: Add an adjustable image grid with optional cell labels for inspecting annotations.
• 2026-11-01: Add support for the RelateAnything object relation prediction model.
• 2026-10-31: Add object-level multi-task attribute annotation with AI-assisted labeling and concurrent inference.
• 2026-10-30: Add support for the Unlimited-OCR-2 model with image and PDF document parsing with multi-page context.
• 2026-10-28: Add support for the WeVisOne-VL model with detection, referring localization, pointing, counting, OCR, text grounding, segmentation, text and box matting, visual prompting, and freeform instructions.
• 2026-10-28: Add support for the Xiaomi-Lens-0 model with referring comprehension and segmentation, multi-instance grounding, reasoning segmentation, embodied referring, and fine-grained perception.
• 2026-10-27: Add annotation display modes: color by label, instance, or group, and a binary mask view with white foreground on a black background.
• 2026-10-27: Add square annotation support.
• 2026-10-24: Add support for smooth zoom, free panning, and boundary clipping on the canvas.
• 2026-10-18: Add support for GroundingPI, GroundAnything, and GroundAnything-VLM with detection, grounding, referring and category-based pointing, OCR, document layout, GUI grounding, and visual prompting.
• 2026-10-01: Add 3D point cloud annotation, supporting 3D point cloud detection and segmentation.
• 2026-08-19: Add support for image tagging, with tag creation, editing, reordering, and batch deletion.
• 2026-08-12: Add support for D-FINE-seg instance segmentation models.
• 2026-08-08: Add support for the RT-DETRv2-OBB rotated object detection model.
• 2026-08-08: Add the Magic Wand tool for quickly creating polygons from contiguous color regions.
• 2026-08-05: Release X-AnyLabeling v4.0.0.
• For more details, please refer to the CHANGELOG

Introduction

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

• Unified support for annotating and processing text, image, video, point cloud, and multimodal data.
• Covers tasks such as 2D image classification, object detection, instance segmentation, pose estimation, oriented object detection, multi-object tracking, optical character recognition, lane annotation, image captioning, visual question answering, document parsing, and 3D point clouds.
• Provides polygons, rectangles, cuboids, rotated boxes, quadrilaterals, circles, lines, polylines, points, masks, and task-specific tools for text detection, text recognition, relation recognition, and KIE.
• Integrates a wide range of state-of-the-art deep learning models for AI-assisted annotation, automated labeling, and batch dataset prediction.
• Supports both local and remote inference through engines and serving frameworks such as ONNX Runtime, TensorRT, OpenCV DNN, vLLM, and SGLang.
• Supports importing and exporting formats such as COCO, VOC, YOLO, DOTA, MOT, MASK, PPOCR, MMGD, VLM-R1, and ShareGPT.
• Runs on Windows, Linux, and macOS, with interfaces available in English, Simplified Chinese, Japanese, and Korean.
• Supports custom model integration, flexible extension, and secondary development.

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
🎨 Semantic Segmentation	U-Net
🏃 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	DepthAnything 1/2
🔗 Relation Recognition	RelateAnything
🧩 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, Unlimited-OCR-2
🗣️ Vision Foundation Models	Rex-Omni, Florence2, LocateAnything, GroundingPI, GroundAnything, GroundAnything-VLM, Xiaomi-Lens-0, WeVisOne-VL
👁️ Vision Language Models	Qwen3-VL, Gemini, ChatGPT, GLM
🛣️ Lane Detection	CLRNet
🔢 Object Counting	CountGD, GeCO, GeCo2
📍 Grounding	Grounding DINO, YOLO-World, YOLOE, SAM 3
📚 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 Parser
11. 3D Point Cloud Annotation

Examples

• Classification
  • Image-Level
  • Shape-Level
• Detection
  • HBB Object Detection
  • OBB Object Detection
• Segmentation
  • Instance Segmentation
  • Binary Semantic Segmentation
  • Multiclass Semantic Segmentation
• Description
  • Tagging
  • Captioning
• Estimation
  • Face Estimation
  • Pose Estimation
  • Depth Estimation
• OCR
  • Text Recognition
  • Key Information Extraction
• Document Parsing
  • PaddleOCR-VL
  • Unlimited-OCR-2
• MOT
  • Tracking by HBB Object Detection
  • Tracking by OBB Object Detection
  • Tracking by Instance Segmentation
  • Tracking by Pose Estimation
• iVOS
  • SAM2-Video
  • SAM3-Video
• Matting
  • Image Matting
• Relation
  • RelateAnything
• VFM
  • Rex-Omni
  • Florence 2
  • LocateAnything
  • WeVisOne-VL
  • Xiaomi-Lens-0
  • GroundingPI / GroundAnything / GroundAnything-VLM
• Counting
  • GeCo
  • GeCo2
• Grounding
  • YOLOE
  • SAM 3
• Training
  • Ultralytics

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

Your sponsorship supports X-AnyLabeling's feature development, model integration, documentation, and community maintenance.

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}}
}