Introduction

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

Features

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.