CVHub520/X-AnyLabeling - Githubissues

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

Segment Anything v2

Tracking by HBB Detection	Tracking by OBB Detection

Tracking by Instance Segmentation	Tracking by Pose Estimation

🥳 What's New

Sep. 2024:
- Release version 2.4.4
- 🚀🚀🚀 Added support for YOLO11-Det/OBB/Pose/Seg/Track model.
- 🧸🧸🧸 Added support for image matting based on RMBG v1.4 model.
- 🔥🔥🔥 Added support for interactive video object tracking based on Segment-Anything-2. [Tutorial]

Click to view more news.

- Aug. 2024: - Release version [2.4.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.4.1) - Support [tracking-by-det/obb/seg/pose](./examples/multiple_object_tracking/README.md) tasks. - Support [Segment-Anything-2](https://github.com/facebookresearch/segment-anything-2) model! (Recommended) - Support [Grounding-SAM2](./docs/en/model_zoo.md) model. - Support lightweight model for Japanese recognition. - Jul. 2024: - Add PPOCR-Recognition and KIE import/export functionality for training PP-OCR task. - Add ODVG import/export functionality for training grounding task. - Add support to annotate KIE linking field. - Support [RT-DETRv2](https://github.com/lyuwenyu/RT-DETR) model. - Support [Depth Anything v2](https://github.com/DepthAnything/Depth-Anything-V2) model. - Jun. 2024: - Support [YOLOv8-Pose](https://docs.ultralytics.com/tasks/pose/) model. - Add [yolo-pose](./docs/en/user_guide.md) import/export functionality. - May. 2024: - Support [YOLOv8-World](https://docs.ultralytics.com/models/yolo-world), [YOLOv8-oiv7](https://docs.ultralytics.com/models/yolov8), [YOLOv10](https://github.com/THU-MIG/yolov10) model. - Release version [2.3.6](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.6). - Add feature to display confidence score. - Mar. 2024: - Release version [2.3.5](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.5). - Feb. 2024: - Release version [2.3.4](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.4). - Enable label display feature. - Release version [2.3.3](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.3). - Release version [2.3.2](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.2). - Support [YOLOv9](https://github.com/WongKinYiu/yolov9) model. - Support the conversion from a horizontal bounding box to a rotated bounding box. - Supports label deletion and renaming. For more details, please refer to the [document](./docs/zh_cn/user_guide.md). - Support for quick tag correction is available; please refer to this [document](./docs/en/user_guide.md) for guidance. - Release version [2.3.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1). - Jan. 2024: - Combining CLIP and SAM models for enhanced semantic and spatial understanding. An example can be found [here](./anylabeling/configs/auto_labeling/edge_sam_with_chinese_clip.yaml). - Add support for the [Depth Anything](https://github.com/LiheYoung/Depth-Anything.git) model in the depth estimation task. - Release version [2.3.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.0). - Support [YOLOv8-OBB](https://github.com/ultralytics/ultralytics) model. - Support [RTMDet](https://github.com/open-mmlab/mmyolo/tree/main/configs/rtmdet) and [RTMO](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose) model. - Release a [chinese license plate](https://github.com/we0091234/Chinese_license_plate_detection_recognition) detection and recognition model based on YOLOv5. - Dec. 2023: - Release version [2.2.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.2.0). - Support [EdgeSAM](https://github.com/chongzhou96/EdgeSAM) to optimize for efficient execution on edge devices with minimal performance compromise. - Support YOLOv5-Cls and YOLOv8-Cls model. - Nov. 2023: - Release version [2.1.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.1.0). - Support [InternImage](https://arxiv.org/abs/2211.05778) model (**CVPR'23**). - Release version [2.0.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.0.0). - Added support for Grounding-SAM, combining [GroundingDINO](https://github.com/wenyi5608/GroundingDINO) with [HQ-SAM](https://github.com/SysCV/sam-hq) to achieve sota zero-shot high-quality predictions! - Enhanced support for [HQ-SAM](https://github.com/SysCV/sam-hq) model to achieve high-quality mask predictions. - Support the [PersonAttribute](https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.5/docs/en/PULC/PULC_person_attribute_en.md) and [VehicleAttribute](https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.5/docs/en/PULC/PULC_vehicle_attribute_en.md) model for multi-label classification task. - Introducing a new multi-label attribute annotation functionality. - Release version [1.1.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v1.1.0). - Support pose estimation: [YOLOv8-Pose](https://github.com/ultralytics/ultralytics). - Support object-level tag with yolov5_ram. - Add a new feature enabling batch labeling for arbitrary unknown categories based on Grounding-DINO. - Oct. 2023: - Release version [1.0.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v1.0.0). - Add a new feature for rotation box. - Support [YOLOv5-OBB](https://github.com/hukaixuan19970627/yolov5_obb) with [DroneVehicle](https://github.com/VisDrone/DroneVehicle) and [DOTA](https://captain-whu.github.io/DOTA/index.html)-v1.0/v1.5/v2.0 model. - SOTA Zero-Shot Object Detection - [GroundingDINO](https://github.com/wenyi5608/GroundingDINO) is released. - SOTA Image Tagging Model - [Recognize Anything](https://github.com/xinyu1205/Tag2Text) is released. - Support YOLOv5-SAM and YOLOv8-EfficientViT_SAM union task. - Support YOLOv5 and YOLOv8 segmentation task. - Release [Gold-YOLO](https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO) and [DAMO-YOLO](https://github.com/tinyvision/DAMO-YOLO) models. - Release MOT algorithms: [OC_Sort](https://github.com/noahcao/OC_SORT) (**CVPR'23**). - Add a new feature for small object detection using [SAHI](https://github.com/obss/sahi). - Sep. 2023: - Release version [0.2.4](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v0.2.4). - Release [EfficientViT-SAM](https://github.com/mit-han-lab/efficientvit) (**ICCV'23**),[SAM-Med2D](https://github.com/OpenGVLab/SAM-Med2D), [MedSAM](https://arxiv.org/abs/2304.12306) and YOLOv5-SAM. - Support [ByteTrack](https://github.com/ifzhang/ByteTrack) (**ECCV'22**) for MOT task. - Support [PP-OCRv4](https://github.com/PaddlePaddle/PaddleOCR) model. - Add `video` annotation feature. - Add `yolo`/`coco`/`voc`/`mot`/`dota` export functionality. - Add the ability to process all images at once. - Aug. 2023: - Release version [0.2.0]((https://github.com/CVHub520/X-AnyLabeling/releases/tag/v0.2.0)). - Release [LVMSAM](https://arxiv.org/abs/2306.11925) and it's variants [BUID](https://github.com/CVHub520/X-AnyLabeling/tree/main/assets/examples/buid), [ISIC](https://github.com/CVHub520/X-AnyLabeling/tree/main/assets/examples/isic), [Kvasir](https://github.com/CVHub520/X-AnyLabeling/tree/main/assets/examples/kvasir). - Support lane detection algorithm: [CLRNet](https://github.com/Turoad/CLRNet) (**CVPR'22**). - Support 2D human whole-body pose estimation: [DWPose](https://github.com/IDEA-Research/DWPose/tree/main) (**ICCV'23 Workshop**). - Jul. 2023: - Add [label_converter.py](./tools/label_converter.py) script. - Release [RT-DETR](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/rtdetr/README.md) model. - Jun. 2023: - Release [YOLO-NAS](https://github.com/Deci-AI/super-gradients/tree/master) model. - Support instance segmentation: [YOLOv8-seg](https://github.com/ultralytics/ultralytics). - Add [README_zh-CN.md](README_zh-CN.md) of X-AnyLabeling. - May. 2023: - Release version [0.1.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v0.1.0). - Release [YOLOv6-Face](https://github.com/meituan/YOLOv6/tree/yolov6-face) for face detection and facial landmark detection. - Release [SAM](https://arxiv.org/abs/2304.02643) and it's faster version [MobileSAM](https://arxiv.org/abs/2306.14289). - Release [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv6](https://github.com/meituan/YOLOv6), [YOLOv7](https://github.com/WongKinYiu/yolov7), [YOLOv8](https://github.com/ultralytics/ultralytics), [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX).

X-AnyLabeling

X-AnyLabeling is a powerful annotation tool that integrates an AI engine for fast and automatic labeling. It’s designed for visual data engineers, offering industrial-grade solutions for complex tasks.

Features

Processes both images and videos.
Accelerates inference with GPU support.
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.
Handles tasks like classification, detection, segmentation, caption, rotation, tracking, estimation, ocr and so on.
Supports diverse annotation styles: polygons, rectangles, rotated boxes, circles, lines, points, and annotations for text detection, recognition, and KIE.

Model library

| **Object Detection** | **SOD with [SAHI](https://github.com/obss/sahi)** | **Facial Landmark Detection** | **Pose Estimation** | | :---: | :---: | :---: | :---: | |

| | **Lane Detection** | **OCR** | **MOT** | **Instance Segmentation** | |

| For more details, please refer to 👉 [model_zoo](./docs/en/model_zoo.md) 👈

Docs

Examples

Contact

If you find this project helpful, please give it a ⭐star⭐, and for any questions or issues, feel free to create an issue or email cv_hub@163.com.

License

This project is released under the GPL-3.0 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}}
}

🔝 Back to Top