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SOTA Zero-Shot Openset Object Detection Model

Strong Image Tagging Model

Powerful Object Segmentation Anything Model

Advanced Multi-Label Classification Model

📄 Table of Contents

• 🥳 What's New
• 👋 Brief Introduction
• 🔥 Highlight
  • 🗝️Key Features
  • ⛏️Model Zoo
• 📖 Tutorials
  • 🔜Quick Start
  • 👨🏼‍💻Build from source
  • 📦Build executable
• 📋 Usage
  • 📜Docs
  • 🧷Hotkeys
• 📧 Contact
• ✅ License
• 🙏🏻 Acknowledgments
• 🏷️ Citing

🥳 What's New ⏏️

• Dec. 2023:
  • 🤗 Release the latest 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.
  • Supoort 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:
  • Add label_converter.py script.
  • Release RT-DETR model.
• Jun. 2023:
  • Release YOLO-NAS model.
  • Support instance segmentation: YOLOv8-seg.
  • Add README_zh-CN.md of X-AnyLabeling.
• May. 2023:
  • Release version 0.1.0.
  • Release YOLOv6-Face for face detection and facial landmark detection.
  • Release SAM and it's faster version MobileSAM.
  • Release YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX.

👋 Brief Introduction ⏏️

X-AnyLabeling stands out as a robust annotation tool seamlessly incorporating an AI inference engine alongside an array of sophisticated features. Tailored for practical applications, it is committed to delivering comprehensive, industrial-grade solutions for image data engineers. This tool excels in swiftly and automatically executing annotations across diverse and intricate tasks.

🔥 Highlight ⏏️

🗝️Key Features

• Supports inference acceleration using GPU.
• Handles both image and video processing.
• Allows single-frame and batch predictions for all tasks.
• Facilitates customization of models and supports secondary development design.
• Enables one-click import and export of mainstream label formats such as COCO, VOC, YOLO, DOTA, MOT, and MASK.
• Covers a range of visual tasks, including classification, detection, segmentation, caption, rotation, tracking, estimation, and ocr.
• Supports various image annotation styles, including polygons, rectangles, rotated boxes, circles, lines, points, as well as annotations for text detection, recognition, and KIE.

⛏️Model Zoo

Object Detection	SOD with SAHI	Facial Landmark Detection	2D Pose Estimation
2D Lane Detection	OCR	MOT	Instance Segmentation
Image Tagging	Grounding DINO	Recognition	Rotation
SAM	BC-SAM	Skin-SAM	Polyp-SAM

For more details, please refer to 👉 model_zoo 👈

📋 Usage ⏏️

📜Docs

🔜Quick Start

📋User Guide

🚀Load Custom Model

🧷Hotkeys

Click to Expand/Collapse

Shortcut	Function
d	Open next file
a	Open previous file
p or [Ctrl+n]	Create polygon
o	Create rotation
r or [Crtl+r]	Create rectangle
i	Run model
q	positive point of SAM mode
e	negative point of SAM mode
c	Quickly clear points of SAM mode
g	Group selected shapes
u	Ungroup selected shapes
s	Hide selected shapes
w	Show selected shapes
Ctrl + q	Quit
Ctrl + i	Open image file
Ctrl + o	Open video file
Ctrl + u	Load all images from a directory
Ctrl + e	Edit label
Ctrl + j	Edit polygon
Ctrl + c	Copy selected shapes
Ctrl + v	Paste selected shapes
Ctrl + d	Duplicate polygon
Ctrl + g	Display overview annotation statistics
Ctrl + h	Toggle visibility shapes
Ctrl + p	Toggle keep previous mode
Ctrl + y	Toggle auto use last label
Ctrl + m	Run all images at once
Ctrl + a	Enable auto annotation
Ctrl + s	Save current annotation
Ctrl + Shift + s	Change output directory
Ctrl -	Zoom out
Ctrl + 0	Zoom to Original
[Ctrl++, Ctrl+=]	Zoom in
Ctrl + f	Fit window
Ctrl + Shift + f	Fit width
Ctrl + z	Undo the last operation
Ctrl + Delete	Delete file
Delete	Delete polygon
Esc	Cancel the selected object
Backspace	Remove selected point
↑→↓←	Keyboard arrows to move selected object
zxcv	Keyboard to rotate selected rect box

📧 Contact ⏏️

🤗 Enjoying this project? Please give it a star! 🤗

If you find this project helpful or interesting, consider starring it to show your support, and if you have any questions or encounter any issues while using this project, feel free to reach out for assistance using the following methods:

• Create an issue
• Email: [email protected]

✅ License ⏏️

This project is released under the GPL-3.0 license.

🙏🏻 Acknowledgments ⏏️

I would like to express my gratitude to the developers and contributors of the following project, whose work has been invaluable in the development of this project:

• LabelMe

• LabelImg

• roLabelImg

• AnyLabeling

• Computer Vision Annotation Tool

I appreciate the dedication and effort put forth by the creators and maintainers of these tools, as they have contributed immensely to the success of this project.

🏷️ Citing ⏏️

BibTeX

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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