CVHub520/X-AnyLabeling
X AnyLabeling
X-AnyLabeling is an AI-assisted annotation platform and computer vision labeling tool. It provides an interface for annotating images and videos using polygons and rectangles to create training sets for machine learning models.
The project distinguishes itself through the integration of external AI models via a plugin-based inference backend, allowing for automated generation of candidate labels and the execution of specialized tasks like pose estimation and object detection. It also functions as an optical character recognition tool for extracting text and layout information from document images.
The platform includes capabilities for dataset format conversion, translating annotations between various industry-standard formats to ensure cross-platform compatibility. It further supports visual data annotation and textual analysis through specialized workflows.
Features
- Visual Annotation Tools - Provides a comprehensive interface for drawing polygons and rectangles on images and videos for computer vision.
- AI-Assisted Labeling - Provides AI-driven generation of candidate labels to automate the manual data annotation process.
- 2D Object Labeling - Runs specialized workflows for complex vision tasks including object detection and pose estimation.
- Computer Vision Annotation - Allows creating high-quality annotated datasets for images and videos for vision AI.
- Custom Model Integrations - Allows the integration of external, user-defined machine learning models through specialized inference backends.
- Inference Backends - Provides a modular inference backend that abstracts the connection between the UI and external AI models.
- Computer Vision Tools - Ships an interactive interface for annotating images and videos with polygons and rectangles.
- Annotation Platforms - Provides a complete software environment for labeling and managing datasets for machine learning training.
- Canvas-Based Annotation UIs - Ships an interactive graphical canvas for drawing precise polygons and shapes on visual data.
- Asynchronous - Implements non-blocking model inference to ensure the user interface remains responsive during heavy AI processing.
- Annotation Format Converters - Provides utilities to convert human-annotated labels into standard formats for machine learning training.
- Optical Character Recognition - Extracts text and layout information from document images using optical character recognition.
- Document Digitization Tools - Converts document images into searchable digital formats by extracting text and layout information.
- Annotation Format Translators - Maps annotation data between various industry standards to ensure compatibility across different machine learning tools.
- Vision Dataset Converters - Provides utilities for translating computer vision annotations between various industry-standard formats to ensure cross-platform compatibility.
- Label Representation Conversion - Translates annotations between different industry-standard data formats to ensure cross-tool compatibility.
- Format Conversions - Implements transformation logic to convert image annotations between various industry-standard formats.
- Dataset Export Mappings - Uses predefined mapping schemas to convert internal annotation objects into specific formats for dataset consumption.
