CNN Research Abstraction Library

The CNN Research Abstraction Library or CRAL in short is a deep learning computer vision library for data scientists, researchers, and developers. With a primary focus on applied deep learning, the CRAL library encourages rapid development and comes with ready-to-use state-of-the-art networks and other pragmatic tools for a variety of applications in the computer vision space.

Our aim is also to make it easier to reproduce and extend the results of various Deep Learning-powered Computer Vision (DLCV) algorithms developed in academia and industrial labs.

List of Algorithms

Object detection

• RetinaNet
• yolov3
• SSD
• FasterRCNN

Instance Segmentation

• MaskRCNN

Semantic Segmentation

• UNet
• UNet ++
• Deeplabv3+
• FpnNet
• PspNet
• SegNet
• LinkNet

Guiding Principles

Simple: To make it easy for deep learning engineers & students alike to use neural networks to build computer vision applications of their choice, using low code approach.

Fast: To accelerate going from experimentation to a working model.

Reproducible: To offer implementations that can easily be trained and reproduced on your own data.

Components

CRAL has a modular design to enable you to use each of its components independently, Alternatively, you can use the pipeline to get started quickly with multiple networks out-of-the-box.

Detailed documentation: Link