This repository is a curated list of web-based demonstrations for various deep vision tasks, serving as a valuable resource for researchers, developers, and enthusiasts exploring the practical applications of computer vision models. It provides direct links to interactive demos, enabling quick evaluation and understanding of different deep learning architectures and their capabilities.

How It Works

The project functions as a directory, aggregating URLs to live web applications that showcase specific deep vision functionalities. These demos cover a wide spectrum of tasks, including image classification, object detection, image segmentation, generative models (GANs, VAEs), style transfer, and more. The curated nature of the list allows users to quickly find and access state-of-the-art or illustrative examples without extensive searching.

Quick Start & Requirements

No installation is required. Users can directly access the demos via the provided URLs.

Highlighted Details

• Covers a broad range of deep vision tasks from hand-written digit recognition to complex image generation and style transfer.
• Includes demos from well-known research institutions and companies like Google, NVIDIA, DeepMind, and Stanford.
• Features interactive tools like Teachable Machine, AutoDraw, and GAN Playground for hands-on experimentation.
• Showcases both classic and more recent deep learning architectures and techniques.

Maintenance & Community

The project is a community-driven effort, with contributions encouraged via pull requests to add new demos. Specific contributors or community channels are not detailed in the README.

Licensing & Compatibility

The repository itself does not appear to have an explicit license mentioned in the README. The licenses of the individual linked demos would need to be checked separately.

Limitations & Caveats

The list is a collection of external links, and the availability, functionality, and performance of the demos are dependent on the original hosting providers. Some links may become outdated or inaccessible over time.