CRAL: Library for CNNs
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
- UNet ++
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.
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.
|CNN models||Ready to use implementations of State-of-the-art (SOTA) algorithms.|
|Pipeline tools||Load and validate your data before you start training.|
|Optimization and debugging||Integration with Experiment Tracking, HP Optimization and other toolsets to help faster and build transparent models|
Detailed documentation: Link
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