CVAT: A Comprehensive Guide on the Computer Vision Annotation Tool
If you are an enthusiast of computer vision and deep learning projects, this would be the perfect platform for you. Welcome to the world of CVAT (Computer Vision Annotation Tool). Developed by Intel corporation, CVAT is an open-source, web-based tool designed specifically to aid in the laborious task of annotating images and videos, thereby benefiting computer vision algorithms and deep learning models.
Project Overview:
CVAT aims to streamline and optimize the process of manual data labelling, a task which acts as a pivotal step in developing robust machine learning models, particularly in computer vision. Its unique characteristic revolves around the fact that it supports 2D and 3D annotation paradigms and can also be performed remotely, assessing a solution to bridge the gap between the ever-increasing demand and availability of high-quality annotated data.
The project primarily targets developers, researchers, students, and companies involved with AI, specifically in the field of computer vision, that rely on well-annotated datasets to train their deep learning models.
Project Features:
One of the standout features of CVAT is its support for a broad spectrum of annotation tasks, from image bounding boxes to polygon annotation and 3D cuboids. CVAT also boasts an interactive and intuitive UI, making it a user-friendly tool across all levels of technical proficiency. Moreover, it features user and task management systems, automatic annotation using AI models, and options for data export to various popular formats like PASCAL VOC, YOLO, COCO, etc.
Technology Stack:
Python and JavaScript form the backbone of CVAT's technology stack, complimented by other technologies including Django, NodeJS, and HTML/CSS for web development and UI, as well as Docker to ensure platform independence. These technologies were strategically chosen to create a universally adaptable and accessible platform that seamlessly cater to the needs of users while maximizing performance.
Project Structure and Architecture:
CVAT follows a well-structured MVC (Model-View-Controller) design pattern, bringing together the frontend and backend architecture. The backend is implemented using Python with Django and manages all data operations. The frontend, developed using JavaScript, NodeJS, and HTML/CSS, connects to the backend through REST API. Docker is utilized to containerize the project, ensuring compatibility across varied platforms.
