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In recent years, we've see an extra-ordinary growth in Computer Vision, with applications in face recognition, image understanding, search, drones, mapping, semi-autonomous and autonomous vehicles. A key part to many of these applications are visual recognition tasks such as image classification, object detection and image similarity. This repository provides examples and best practice guidelines for building computer vision systems. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in Computer Vision algorithms, neural architectures, and operationalizing such systems. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud.
Features
- The example notebooks would serve as guidelines and showcase best practices and usage of the tools in a wide variety of languages
- These examples are provided as Jupyter notebooks and common utility functions
- All examples use PyTorch as the underlying deep learning library
- This repository supports various Computer Vision scenarios which either operate on a single image
- Supports scenarios such as action recognition which take a video sequence as input
- The utilities and examples provided are intended to be solution accelerators for real-world vision problems
Project Samples
