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.
| Components | Description |
|---|---|
| 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 |