Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Overview
flax

Flax Models

A collection of pretrained models in Flax.

About

The goal of this project is to make current deep learning models more easily available for the awesome Jax/Flax ecosystem.

Models

Example Notebooks to play with on Colab

Installation

You will need Python 3.7 or later.

  1. For GPU usage, follow the Jax installation with CUDA.
  2. Then install:
    > pip install --upgrade git+https://github.com/matthias-wright/flaxmodels.git

For CPU-only you can skip step 1.

Documentation

The documentation for the models is on the individual model pages.

Testing

To run the tests, pytest needs to be installed.

> git clone https://github.com/matthias-wright/flaxmodels.git
> cd flaxmodels
> python -m pytest tests/

Acknowledgments

Thank you to the developers of Jax and Flax. The title image is a photograph of a flax flower, kindly made available by Marta Matyszczyk.

License

Each model has an individual license.

Owner
Matthias Wright
PhD Student in Computer Vision @ Heidelberg University
Matthias Wright
Neural Articulated Radiance Field

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Machine learning Bot detection technique, based on United States election dataset

Machine learning Bot detection technique, based on United States election dataset (2020). Current github repo provides implementation described in pap

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Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

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Code accompanying the paper "How Tight Can PAC-Bayes be in the Small Data Regime?"

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Stock-Prediction- In this project, we aim to enhance the prediction of stock market movements using sentiment analysis and deep learning. We divide th

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Adversarial Autoencoders

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