microsoft/VoTTArchived
VoTT
VoTT is a computer vision annotation software and machine learning dataset preparation tool. It is a desktop application designed for drawing bounding boxes and assigning tags to objects in images and videos to create training datasets for object detection models.
The application utilizes a cross-platform desktop interface to manage image and video assets. It features a local-first storage integration to handle large media assets directly from the host machine's file system and includes frame-rate controlled video sampling to extract specific images from video streams for labeling.
The software covers a full data lifecycle, including asset import from local or cloud storage and the conversion of annotated data into various machine learning formats via schema-based exports. It also incorporates token-based encryption to secure sensitive project configuration settings.
Features
- Bounding Box Interfaces - Provides a specialized graphical interface for drawing rectangular bounding boxes around objects for detection model training.
- Annotation Format Exporters - Converts internal annotation coordinates into standardized machine learning dataset formats via template-based exporters.
- 2D Object Labeling - Provides a tool for creating rectangular bounding box annotations on 2D images and video frames for object detection.
- Computer Vision Annotation - Serves as a comprehensive tool for creating high-quality annotated datasets from images and videos for vision AI.
- Data Labeling Tools - Provides interfaces and workflows for annotating datasets to support supervised machine learning tasks.
- Dataset Preparation Tools - Provides utilities for collecting, cleaning, and curating image and video samples for machine learning model training.
- Data Preparation Tools - Includes utilities to clean, format, and organize raw media assets into structures suitable for ML model ingestion.
- Dataset Exports - Exports image and video samples and their associated annotations into specific formats for AI training pipelines.
- Local-First Storage - Prioritizes direct host machine file system integration to manage large media assets without requiring server uploads.
- Image Labeling - Allows users to define object locations and assign tags within images to generate ground truth training data.
- Video Frame Annotations - Supports tagging objects across video sequences by navigating through frames to ensure consistency for ML models.
- Frame Annotation Workflows - Enables tagging of objects across video sequences by navigating through extracted frames to maintain temporal consistency.
- Media Asset Importers - Connects to local file systems and cloud storage providers to import images and videos for the labeling pipeline.
- Electron Desktop Applications - Implements a cross-platform desktop application using the Electron framework to integrate web technologies with native system access.
- Frame Sampling Strategies - Implements specific sampling strategies to extract a sequence of labelable images from video streams at set intervals.
- Canvas-Based Annotation UIs - Provides a graphical canvas interface for drawing bounding boxes and tagging objects in real-time on images and video frames.
- Desktop Shells - Uses an Electron shell to wrap the web interface, enabling native desktop window management and local file system access.
- Image Annotation - Cross-platform utility for labeling image and video assets.
- Image Annotation Tools - Tool for labeling and annotating images for object detection models.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
