VisionLab

This project proposes the development of a Computer Vision Training Platform that enables users to seamlessly annotate, label, train, and deploy custom computer vision models. The platform will provide a comprehensive solution for users to manage their datasets, select or upload models, and perform real-time inferencing with detection-based actions.

The proposed platform will feature an intuitive image annotation and labeling interface, allowing users to create high-quality datasets for training various models. Users will have the option to train models locally(using LOCAL-GPU client of us) or on cloud-based servers, providing flexibility based on resource availability. The platform will support popular pre-trained models such as YOLO,ResNet, COCO, and custom models, ensuring adaptability to diverse user requirements.

A key innovation of this platform is its ability to perform real-time inferencing by connecting to external cameras. Users can set detection-based actions, such as triggering alerts or controlling robotic movements (e.g., turning, moving forward/backward), based on detection results. Additionally, the platform will integrate 3D model simulations, allowing users to visualize and simulate detection-based actions before real-world deployment.

This project aims to provide a scalable, efficient, and user-friendly computer vision solution for applications in robotics, automation, security, and manufacturing. The platform will serve as a valuable tool for developers and researchers, bridging the gap between computer vision theory and practical implementation, thereby fostering innovation and enhancing productivity in AI-driven applications.