vit-tensorflow

TensorFlow wrapper of Vision Transformer from the paper "An Image Is Worth 16x16 Words" by Dosovitskiy et al. that's currently under review for ICLR 2021.

The original jax implementation can be found on the Google Research repo here.

Why

Inspired by Phil Wang's vit-pytorch wrapper, I hoped to build something similar in TensorFlow. Besides, it's a cool side-project to embark on!

Installation

You can install vit-tensorflow via pip:

pip install vit-tensorflow

Usage

As vit-tensorflow is a wrapper, you can use the model off-the-shelf in your pipelines.

import tensorflow as tf
from vit_tensorflow import ViT

vit = ViT(
    image_size=28,
    patch_size=32,
    heads=8,
    n_classes=10,
    mlb_dim=2048,
    dropout=0.1
)

img = tf.random.uniform([28, 28], 0, 1)
logits = vit(img) # outputs a (1000, 1) vector

Notes

As of now, I'm still trying to figure out how to enable users to train/finetune the model. So far, it only allows for inference.

Citations

@misc{dosovitskiy2020image,
    title   = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
    author  = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
    year    = {2020},
    eprint  = {2010.11929},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{vaswani2017attention,
    title   = {Attention Is All You Need},
    author  = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
    year    = {2017},
    eprint  = {1706.03762},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

License

MIT