<div align="center">
  <p>
    <a href="https://github.com/CVHub520/X-AnyLabeling/" target="_blank">
      <img alt="X-AnyLabeling" height="200px" src="https://github.com/user-attachments/assets/0714a182-92bd-4b47-b48d-1c5d7c225176"></a>
  </p>

[简体中文](README_zh-CN.md) | [English](README.md)

</div>

<p align="center">
    <a href="./LICENSE"><img src="https://img.shields.io/badge/License-LGPL%20v3-blue.svg"></a>
    <a href=""><img src="https://img.shields.io/github/v/release/CVHub520/X-AnyLabeling?color=ffa"></a>
    <a href=""><img src="https://img.shields.io/badge/python-3.10+-aff.svg"></a>
    <a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
    <a href=""><img src="https://img.shields.io/github/downloads/CVHub520/X-AnyLabeling/total?label=downloads"></a>
    <a href="https://modelscope.cn/collections/X-AnyLabeling-7b0e1798bcda43"><img src="https://img.shields.io/badge/modelscope-X--AnyLabeling-6750FF?link=https%3A%2F%2Fmodelscope.cn%2Fcollections%2FX-AnyLabeling-7b0e1798bcda43"></a>
</p>

![](https://user-images.githubusercontent.com/18329471/234640541-a6a65fbc-d7a5-4ec3-9b65-55305b01a7aa.png)

<img src="https://github.com/user-attachments/assets/8b5f290a-dddf-410c-a004-21e5a7bcd1cc" width="100%" />

<details>
<summary><strong>自动训练</strong></summary>

<video src="https://github.com/user-attachments/assets/c0ab2056-2743-4a2c-ba93-13f478d3481e" width="100%" controls>
</video>
</details>

<details>
<summary><strong>自动标注</strong></summary>

<video src="https://github.com/user-attachments/assets/f517fa94-c49c-4f05-864e-96b34f592079" width="100%" controls>
</video>
</details>

<details>
<summary><strong>检测一切</strong></summary>

<img src="https://github.com/user-attachments/assets/7f43bcec-96fd-48d1-bd36-9e5a440a66f6" width="100%" />
</details>

<details>
<summary><strong>分割一切</strong></summary>

<img src="https://github.com/user-attachments/assets/208dc9ed-b8c9-4127-9e5b-e76f53892f03" width="100%" />
</details>

<details>
<summary><strong>可提示概念定位</strong></summary>

<video src="https://github.com/user-attachments/assets/52cbdb5d-cc60-4be5-826f-903ea4330ca8" width="100%" controls>
</video>
</details>

<details>
<summary><strong>视觉问答</strong></summary>

<video src="https://github.com/user-attachments/assets/53adcff4-b962-41b7-a408-3afecd8d8c82" width="100%" controls>
</video>
</details>

<details>
<summary><strong>聊天机器人</strong></summary>

<img src="https://github.com/user-attachments/assets/56c9a20b-c836-47aa-8b54-bad5bb99b735" width="100%" />
</details>

<details>
<summary><strong>图像分类器</strong></summary>

<video src="https://github.com/user-attachments/assets/0652adfb-48a4-4219-9b18-16ff5ce31be0" width="100%" controls>
</video>
</details>

## 🥳 新功能

<video src="https://github.com/user-attachments/assets/493183fd-6cbe-45fb-9808-ec2b0af7a0f9" width="100%" controls>
</video>

<video src="https://github.com/user-attachments/assets/4a676ebf-d2ae-4327-b078-8e63a5323793" width="100%" controls>
</video>

- 新增 [PP-DocLayoutV3](./examples/optical_character_recognition/document_layout_analysis/README.md)，支持多点定位（四边形/多边形）和逻辑阅读顺序预测
- 新增 [PaddleOCR-VL-1.5](./examples/optical_character_recognition/multi_task/README.md)，支持 OCR、表格识别、公式识别、图表识别、文本检测与识别及印章识别
- 新增[YOLO26](https://github.com/ultralytics/ultralytics)系列模型，支持目标检测、实例分割、姿态估计和旋转检测
- 新增对比视图功能，支持分屏图像对比，适用于红外/可见光融合、掩码预览、图像超分等场景 [[教程](./docs/zh_cn/user_guide.md#36-对比视图)]
- 新增[YOLO26](https://github.com/ultralytics/ultralytics)目标检测模型
- 新增多模态大型语言模型[Rex-Omni](https://github.com/IDEA-Research/Rex-Omni)，支持目标检测、关键点、指代、OCR和视觉提示等多种视觉任务 [[教程](./examples/vision_language/rexomni/README.md)]
- 新增强大的文件搜索功能，支持文本搜索、正则表达式搜索和属性搜索 [[教程](./docs/zh_cn/user_guide.md#25-搜索图片)]
- 新增多边形、矩形、旋转框和圆形对象的半透明掩码渲染功能，支持快捷键切换显示（`Ctrl+M`）
- 新增基于 Segment Anything 3 的一键文本和视觉提示视频检测及分割跟踪 [[教程](./examples/interactive_video_object_segmentation/sam3/README.md)]
- 更多详情，请参考[更新日志](./CHANGELOG.md)

## 简介

**X-AnyLabeling** 是一款基于AI推理引擎和丰富功能特性于一体的强大辅助标注工具，其专注于实际应用，致力于为多模态数据工程师提供工业级的一站式解决方案，可自动快速进行各种复杂任务的标定。

<img src="https://github.com/user-attachments/assets/632e629b-0dec-407b-95a6-728052e1dd7b" width="100%" />

此外，我们强烈推荐您尝试 [X-AnyLabeling-Server](https://github.com/CVHub520/X-AnyLabeling-Server)，这是一个简单、轻量级且可扩展的框架，为 X-AnyLabeling 提供远程推理能力。

## 新特性

<img src="https://github.com/user-attachments/assets/c65db18f-167b-49e8-bea3-fcf4b43a8ffd" width="100%" />

- 支持远程推理服务。
- 支持`GPU`、`FFmpeg`加速推理。
- 支持一键预测所有图像。
- 支持`图像`和`视频`处理。
- 支持自定义模型和二次开发。
- 支持一键导入和导出多种标签格式，如 COCO\VOC\YOLO\DOTA\MOT\MASK\PPOCR\MMGF\VLM-R1 等；
- 支持多种图像标注样式，包括 `多边形`、`矩形`、`旋转框`、`圆形`、`线条`、`点`，以及 `文本检测`、`识别` 和 `KIE` 标注；
- 支持各类视觉任务，如`图像分类`、`目标检测`、`实例分割`、`姿态估计`、`旋转检测`、`多目标跟踪`、`光学字符识别`、`图像文本描述`、`车道线检测`、`分割一切`、`视觉问答`等。

### 模型库

<img src="https://github.com/user-attachments/assets/7da2da2e-f182-4a1b-85f6-bfd0dfcc6a1b" width="100%" />

| **任务类别** | **支持模型** |
| :--- | :--- |
| 🖼️ **图像分类** | YOLOv5-Cls, YOLOv8-Cls, YOLO11-Cls, InternImage, PULC |
| 🎯 **目标检测** | YOLOv5/6/7/8/9/10, YOLO11/12/26, YOLOX, YOLO-NAS, D-FINE, DAMO-YOLO, Gold_YOLO, RT-DETR, RF-DETR, DEIMv2 |
| 🖌️ **实例分割** | YOLOv5-Seg, YOLOv8-Seg, YOLO11-Seg, YOLO26-Seg, Hyper-YOLO-Seg, RF-DETR-Seg |
| 🏃 **姿态估计** | YOLOv8-Pose, YOLO11-Pose, YOLO26-Pose, DWPose, RTMO |
| 👣 **目标跟踪** | Bot-SORT, ByteTrack, SAM2/3-Video |
| 🔄 **旋转目标检测** | YOLOv5-Obb, YOLOv8-Obb, YOLO11-Obb, YOLO26-Obb |
| 📏 **深度估计** | Depth Anything |
| 🧩 **分割一切** | SAM 1/2/3, SAM-HQ, SAM-Med2D, EdgeSAM, EfficientViT-SAM, MobileSAM |
| ✂️ **图像抠图** | RMBG 1.4/2.0 |
| 💡 **候选框提取** | UPN |
| 🏷️ **图像标记** | RAM, RAM++ |
| 📄 **光学字符识别** | PP-OCRv4, PP-OCRv5, PP-DocLayoutV3, PaddleOCR-VL-1.5 |
| 🗣️ **视觉基础模型** | Rex-Omni, Florence2 |
| 👁️ **视觉语言模型** | Qwen3-VL, Gemini, ChatGPT, GLM |
| 🛣️ **车道线检测** | CLRNet |
| 📍 **Grounding** | CountGD, GeCO, Grounding DINO, YOLO-World, YOLOE |
| 📚 **其他** | 👉 [model_zoo](./docs/en/model_zoo.md) 👈 |

## 文档

0. [远程推理服务](https://github.com/CVHub520/X-AnyLabeling-Server)
1. [安装文档](./docs/zh_cn/get_started.md)
2. [用户手册](./docs/zh_cn/user_guide.md)
3. [命令行界面](./docs/zh_cn/cli.md)
4. [自定义模型](./docs/zh_cn/custom_model.md)
5. [常见问题答疑](./docs/zh_cn/faq.md)
6. [聊天机器人](./docs/zh_cn/chatbot.md)
7. [视觉问答](./docs/zh_cn/vqa.md)
8. [多类别图像分类器](./docs/en/image_classifier.md)

<img src="https://github.com/user-attachments/assets/0d67311c-f441-44b6-9ee0-932f25f51b1c" width="100%" />

## 示例

- [Classification](./examples/classification/)
  - [Image-Level](./examples/classification/image-level/README.md)
  - [Shape-Level](./examples/classification/shape-level/README.md)
- [Detection](./examples/detection/)
  - [HBB Object Detection](./examples/detection/hbb/README.md)
  - [OBB Object Detection](./examples/detection/obb/README.md)
- [Segmentation](./examples/segmentation/README.md)
  - [Instance Segmentation](./examples/segmentation/instance_segmentation/)
  - [Binary Semantic Segmentation](./examples/segmentation/binary_semantic_segmentation/)
  - [Multiclass Semantic Segmentation](./examples/segmentation/multiclass_semantic_segmentation/)
- [Description](./examples/description/)
  - [Tagging](./examples/description/tagging/README.md)
  - [Captioning](./examples/description/captioning/README.md)
- [Estimation](./examples/estimation/)
  - [Pose Estimation](./examples/estimation/pose_estimation/README.md)
  - [Depth Estimation](./examples/estimation/depth_estimation/README.md)
- [OCR](./examples/optical_character_recognition/)
  - [Text Recognition](./examples/optical_character_recognition/text_recognition/)
  - [Key Information Extraction](./examples/optical_character_recognition/key_information_extraction/README.md)
- [MOT](./examples/multiple_object_tracking/README.md)
  - [Tracking by HBB Object Detection](./examples/multiple_object_tracking/README.md)
  - [Tracking by OBB Object Detection](./examples/multiple_object_tracking/README.md)
  - [Tracking by Instance Segmentation](./examples/multiple_object_tracking/README.md)
  - [Tracking by Pose Estimation](./examples/multiple_object_tracking/README.md)
- [iVOS](./examples/interactive_video_object_segmentation)
  - [SAM2-Video](./examples/interactive_video_object_segmentation/sam2/README.md)
  - [SAM3-Video](./examples/interactive_video_object_segmentation/sam3/README.md)
- [Matting](./examples/matting/)
  - [Image Matting](./examples/matting/image_matting/README.md)
- [Vision-Language](./examples/vision_language/)
  - [Rex-Omni](./examples/vision_language/rexomni/README.md)
  - [Florence 2](./examples/vision_language/florence2/README.md)
- [Counting](./examples/counting/)
  - [GeCo](./examples/counting/geco/README.md)
- [Grounding](./examples/grounding/)
  - [YOLOE](./examples/grounding/yoloe/README.md)
  - [SAM 3](./examples/grounding/sam3/README.md)
- [Training](./examples/training/)
  - [Ultralytics](./examples/training/ultralytics/README.md)

## 贡献指南

我们欢迎社区协作！**X‑AnyLabeling** 项目的成长离不开开发者们的共同参与，无论是修复 Bug、优化文档、还是添加新功能，您的贡献都非常宝贵。

在参与前请阅读我们的 [贡献指南](./CONTRIBUTING.md)，并在提交 Pull Request 前确认您已同意 [贡献者许可协议 (CLA)](./CLA.md)。

如果你觉得这个项目有帮助，请点亮右上角的⭐星标⭐。如有任何问题或疑问，欢迎[创建 issue](https://github.com/CVHub520/X-AnyLabeling/issues) 或发送邮件至 cv_hub@163.com。

衷心感谢每一位为项目贡献力量的朋友 🙏

## 许可

本项目遵循 [GPL-3.0 license](./LICENSE) 协议，完全开源免费，初衷是希望让更多开发者、研究者和企业能够便捷地使用 AI 应用平台，推动整个行业的发展。我们鼓励大家自由使用（包括商业用途），也可以基于本项目添加功能并进行商业化，但必须保留品牌标识并标注源项目地址。

此外，为了了解 X-AnyLabeling 的生态和使用情况，如果您将本项目用于学术、科研、教学或是企业用户，请在此[填写登记表](https://forms.gle/MZCKhU7UJ4TRSWxR7)。此登记仅用于统计使用情况，不会产生任何费用，我们会对信息严格保密。

X-AnyLabeling 由个人独立开发和维护。如果本项目对您有所帮助，欢迎通过下方捐赠链接支持项目持续发展，您的支持是对我最大的鼓励！如对项目有任何疑问或希望合作，欢迎随时微信联系：ww10874。

## 赞助

| **微信支付** | **支付宝** |
| :---: | :---: |
| <img src="https://github.com/user-attachments/assets/0178cf76-3627-426e-8432-ec031c9278ae" width="400px" height="400px" style="object-fit: contain;" /> | <img src="https://github.com/user-attachments/assets/87544ff8-3560-4696-b035-1fd26ecd162b" width="400px" height="400px" style="object-fit: contain;" /> |

感谢您的支持！

## 引用

如果您在研究中使用了这个软件，请按照以下方式引用它：

```
@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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