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🥳 What's New

• Oct. 2024:
  • ✨✨✨ Added support for DocLayout-YOLO model.
• 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]

• Aug. 2024:
  • Release version 2.4.1
  • Support tracking-by-det/obb/seg/pose tasks.
  • Support Segment-Anything-2 model! (Recommended)
  • Support Grounding-SAM2 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 model.
  • Support Depth Anything v2 model.
• Jun. 2024:
  • Support YOLOv8-Pose model.
  • Add yolo-pose import/export functionality.
• May. 2024:
  • Support YOLOv8-World, YOLOv8-oiv7, YOLOv10 model.
  • Release version 2.3.6.
  • Add feature to display confidence score.
• Mar. 2024:
  • Release version 2.3.5.
• 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:
  • 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.

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

For more details, please refer to 👉 model_zoo 👈

Docs

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

Examples

• Classification
  • Image-Level
  • Shape-Level
• Detection
  • HBB Object Detection
  • OBB Object Detection
• Segmentation
  • Instance Segmentation
  • Binary Semantic Segmentation
  • Multiclass Semantic Segmentation
• Description
  • Tagging
  • Captioning
• Estimation
  • Pose Estimation
  • Depth Estimation
• OCR
  • Text Recognition
  • Key Information Extraction
• MOT
  • Tracking by HBB Object Detection
  • Tracking by OBB Object Detection
  • Tracking by Instance Segmentation
  • Tracking by Pose Estimation
• iVOS
• Matting
  • Image Matting

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}}
}