Skip to content

Effortless data labeling with AI support from Segment Anything and other awesome models.

License

Notifications You must be signed in to change notification settings

crain-cn/X-AnyLabeling

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Segment Anything 2.1

Open Vision

Interactive Visual-Text Prompting for Generic Vision Tasks

Tracking by HBB Detection Tracking by OBB Detection
Tracking by Instance Segmentation Tracking by Pose Estimation

🥳 What's New


Click to view more news.
  • Aug. 2024:
  • 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 model.
    • Support Depth Anything v2 model.
  • Jun. 2024:
  • May. 2024:
  • Mar. 2024:
  • Feb. 2024:
    • Release version 2.3.4.
    • Enable label display feature.
    • Release version 2.3.3.
    • Release version 2.3.2.
    • Support 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.
    • Support for quick tag correction is available; please refer to this document for guidance.
    • Release version 2.3.1.
  • Jan. 2024:
    • Combining CLIP and SAM models for enhanced semantic and spatial understanding. An example can be found here.
    • Add support for the Depth Anything model in the depth estimation task.
    • Release version 2.3.0.
    • Support YOLOv8-OBB model.
    • Support RTMDet and RTMO model.
    • Release a chinese license plate detection and recognition model based on YOLOv5.
  • Dec. 2023:
    • Release version 2.2.0.
    • Support 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.
    • Support InternImage model (CVPR'23).
    • Release version 2.0.0.
    • Added support for Grounding-SAM, combining GroundingDINO with HQ-SAM to achieve sota zero-shot high-quality predictions!
    • Enhanced support for HQ-SAM model to achieve high-quality mask predictions.
    • Support the PersonAttribute and VehicleAttribute model for multi-label classification task.
    • Introducing a new multi-label attribute annotation functionality.
    • Release version 1.1.0.
    • Support pose estimation: YOLOv8-Pose.
    • 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.
    • Add a new feature for rotation box.
    • Support YOLOv5-OBB with DroneVehicle and DOTA-v1.0/v1.5/v2.0 model.
    • SOTA Zero-Shot Object Detection - GroundingDINO is released.
    • SOTA Image Tagging Model - Recognize Anything is released.
    • Support YOLOv5-SAM and YOLOv8-EfficientViT_SAM union task.
    • Support YOLOv5 and YOLOv8 segmentation task.
    • Release Gold-YOLO and DAMO-YOLO models.
    • Release MOT algorithms: OC_Sort (CVPR'23).
    • Add a new feature for small object detection using SAHI.
  • Sep. 2023:
    • Release version 0.2.4.
    • Release EfficientViT-SAM (ICCV'23),SAM-Med2D, MedSAM and YOLOv5-SAM.
    • Support ByteTrack (ECCV'22) for MOT task.
    • Support PP-OCRv4 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.
    • Release LVMSAM and it's variants BUID, ISIC, Kvasir.
    • Support lane detection algorithm: CLRNet (CVPR'22).
    • Support 2D human whole-body pose estimation: DWPose (ICCV'23 Workshop).
  • Jul. 2023:
  • Jun. 2023:
  • May. 2023:

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 Facial Landmark Detection Pose Estimation
Lane Detection OCR MOT Instance Segmentation
Tagging Grounding Recognition Rotation
Segment Anything BC-SAM Skin-SAM Polyp-SAM

For more details, please refer to 👉 model_zoo 👈

Docs

  1. Installation & Quickstart
  2. Usage
  3. Customize a model

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 [email protected].

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}}
}

About

Effortless data labeling with AI support from Segment Anything and other awesome models.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 96.4%
  • Cuda 3.2%
  • Other 0.4%