Computer Vision

A highly optimized library with focus on real-time applications. OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It has C++, Python, Java, and MATLAB interfaces and supports Windows, Linux, Android, and Mac OS.

A framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. It includes a collection of pre-trained models that have been trained on various image recognition tasks, including the COCO dataset, Kitti dataset, and the Open Images dataset.

A state-of-the-art, real-time object detection system that is able to recognize objects in images and video at high speeds. YOLO is novel because it frames object detection as a single regression problem, straight from image pixels to bounding box coordinates and class probabilities.

A part of the PyTorch project, PyTorch Vision is a library that contains popular datasets, model architectures, and common image transformations for computer vision. It aims to provide a flexible and easy-to-use tool for computer vision research and development.

Built on PyTorch, FastAI simplifies training neural networks using modern best practices. It's designed to support both deep learning and computer vision with a high-level API, making it accessible for beginners and flexible enough for researchers.

A state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., cat, dog, car) to every pixel in the input image. Developed by Google, it's designed to improve the accuracy of image segmentation, especially at object edges.