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SplitMixer: Fat Trimmed From MLP-like Models (Arxiv)

PyTorch implementation of the SplitMixer MLP model for visual recognition

Code overview

The most important code is in splitmixer.py. We trained SplitMixers (on ImageNet) using the timm framework, which we copied from here.

For CIFAR-{10,100} trainings or standalone model definitions, please refer to the cifar notebook.

Inside pytorch-image-models, we have made the following modifications:

Added ConvMixers
- added timm/models/splitmixer.py
- modified timm/models/__init__.py

Evaluation

CIFAR-10

Patch Size p=2, Kernel Size k=5

Model Name	Params (M)	FLOPS (M)	Acc
ConvMixer-256/8	0.60	152.6	94.17
SplitMixer-I 256/8	0.28	71.8	93.91
SplitMixer-II 256/8	0.17	46.2	92.25
SplitMixer-III 256/8	0.17	79.8	92.52
SplitMixer-IV 256/8	0.31	79.8	93.38

CIFAR-100

Patch Size p=2, Kernel Size k=5

Model Name	Params (M)	FLOPS (M)	Acc
ConvMixer-256/8	0.62	152.6	73.92
Splitixer-I 256/8	0.30	71.9	72.88
SplitMixer-II 256/8	0.19	46.2	70.44
SplitMixer-III 256/8	0.19	79.8	70.89
SplitMixer-IV 256/8	0.32	79.8	71.75

Flowers102

Patch Size p=7, Kernel Size k=7

Model Name	Params (M)	FLOPS (M)	Acc
ConvMixer-256/8	0.70	696	60.47
Splitixer-I 256/8	0.34	331	62.03
SplitMixer-II 256/8	0.24	229	59.33
SplitMixer-III 256/8	0.24	363	59.00
SplitMixer-IV 256/8	0.37	363	61.51

Foods101

Patch Size p=7, Kernel Size k=7

Model Name	Params (M)	FLOPS (M)	Acc
ConvMixer-256/8	0.70	696	74.59
Splitixer-I 256/8	0.34	331	73.56
SplitMixer-II 256/8	0.24	229	71.74
SplitMixer-III 256/8	0.24	363	72.78
SplitMixer-IV 256/8	0.37	363	72.92

ImageNet

Stay Tuned!

Citation

If you use this code in your research, please cite this project.

@inproceedings{borji2022SplitMixer,
title={SplitMixer: Fat Trimmed From MLP-like Models},
author={Ali Borji and Sikun Lin},
booktitle={Arxiv},
year={2022},
url={https://arxiv.org/pdf/2207.10255.pdf}
}