ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on patches as input, separates the mixing of spatial and channel dimensions, and maintains equal size and resolution throughout the network. In contrast, however, the ConvMixer uses only standard convolutions to achieve the mixing steps. Despite its simplicity, we show that the ConvMixer outperforms the ViT, MLP-Mixer, and some of their variants for similar parameter counts and data set sizes, in addition to outperforming classical vision models such as the ResNet.
Official GitHub Link: https://github.com/tmp-iclr/convmixer
Paper Link: https://openreview.net/pdf?id=TVHS5Y4dNvM
Note: Paper is under review for ICLR 2022
pip install -q tensorflow-addons
Note: We are using TensorFlow-Addons for using the AdamW optimizer and GeLU activation function.
TensorBoard Link: https://tensorboard.dev/experiment/bkhqOz0RQ1Cv5dwrDQySMQ/
Note: Trained 25 Epochs and got a top-5-accuracy of 64.41%
- To train on 150 epochs
- To train model on ImageNet dataset
@inproceedings{
anonymous2022patches,
title={Patches Are All You Need?},
author={Anonymous},
booktitle={Submitted to The Tenth International Conference on Learning Representations },
year={2022},
url={https://openreview.net/forum?id=TVHS5Y4dNvM},
note={under review}
}
MIT License
Copyright (c) 2021 Sayan Nath
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