Fireball

Fireball is a Deep Neural Network (DNN) library for creating, training, evaluating, quantizing, and compressing DNN based models across a range of applications. Here is a summary of main features:

• Easily create any network structure using a limited set of fundamental building blocks chained together in a text string.
• Create models for classification, regression, object detection, and NLP applications.
• Add functionality by creating your own "Blocks" and reuse them in your network structure.
• Define your own layer types or loss functions and use them in the network structure.
• Apply Low-Rank decomposition on layers of your model to reduce the number of network parameters.
• Apply Pruning to the network parameters.
• Apply K-Means quantization on network parameters to further reduce the size of model.
• Retrain your model after applying low-rank decomposition, pruning, and/or quantization.
• Compress models using arithmetic entropy coding.
• Export the models to ONNX, Tensorflow, or CoreML even after applying low-rank decomposition, pruning, and/or quantization.

Fireball Documentation

• Installation
• Documentation Home
• Fireball Layers
• Fireball API

Playgrounds

The Playgrounds folder contains a set of tutorials explaining how to use Fireball for some common deep learning models such as object detection and NLP tasks.

Getting started with Fireball Playgrounds

Authors

• Shahab Hamidi-Rad, InterDigital AI Lab.