https://github.com/interdigitalinc/fireball

Deep Neural Network Library
https://github.com/interdigitalinc/fireball

Last synced: about 1 month ago
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Deep Neural Network Library

• Host: GitHub
• URL: https://github.com/interdigitalinc/fireball
• Owner: InterDigitalInc
• License: other
• Created: 2021-10-06T20:44:54.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2024-02-19T18:22:19.000Z (10 months ago)
• Last Synced: 2024-04-24T12:48:38.048Z (8 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 43.6 MB
• Stars: 9
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
![ID-Fireball-logo](Playgrounds/Fireball.png)

# 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](https://interdigitalinc.github.io/Fireball/html//source/installation.html)

* [Documentation Home](https://interdigitalinc.github.io/Fireball/html/)

* [Fireball Layers](https://interdigitalinc.github.io/Fireball/html//source/layers.html)

* [Fireball API](https://interdigitalinc.github.io/Fireball/html//source/model.html)

## 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](Playgrounds/README.md)

## Authors

* Shahab Hamidi-Rad, InterDigital AI Lab.