📄 Table of Contents

• 🥳 What's New
• 👋 Brief Introduction
• 🔥 Highlight
  • 🗝️Key Features
  • ⛏️Model Zoo
• 📋 Usage
  • 📜 Docs
  • 🔜Quick Start
  • 📋User Guide
  • 🚀Load Custom Model
  • 🧷Hotkeys
• 📧 Contact
• ✅ License
• 🙏🏻 Acknowledgments
• 🏷️ Citing

🥳 What's New ⏏️

• Mar. 2024:
  • 🤗 Release the latest version 2.3.5 🤗
• Feb. 2024:
  • Release version 2.3.4.
  • Enable label display feature.
  • Release version 2.3.3.
  • ✨✨✨ Support YOLO-World model.
  • 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.
  • 🔥🔥🔥 Adding 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:
  • 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](https://github.com/obss/sahi)** | **Facial Landmark Detection** | **2D Pose Estimation** | | :---: | :---: | :---: | :---: | | | | | | | **2D Lane Detection** | **OCR** | **MOT** | **Instance Segmentation** | | | | | | | **Image Tagging** | **Grounding DINO** | **Recognition** | **Rotation** | | | | | | | **[SAM](https://segment-anything.com/)** | **BC-SAM** | **Skin-SAM** | **Polyp-SAM** | | | | | | For more details, please refer to 👉 [model_zoo](./docs/en/model_zoo.md) 👈

📋 Usage ⏏️

• 📜Docs

  • ##### 🔜Quick Start
  • ##### 📋User Guide
  • ##### 🚀Load Custom Model

• 🧷Hotkeys

📧 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: cv_hub@163.com

✅ License ⏏️

This project is released under the GPL-3.0 license.

🙏🏻 Acknowledgments ⏏️

I extend my heartfelt thanks to the developers and contributors of the projects LabelMe, LabelImg, roLabelImg, AnyLabeling, and Computer Vision Annotation Tool. Their dedication and contributions have played a crucial role in shaping 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}} }