MLP-Mixer

Unoffical Implementation of MLP-Mixer, easy to use with terminal. Train and test easly.

https://arxiv.org/abs/2105.01601

MLP-Mixer is an architecture based exclusively on multi-layer perceptrons (MLPs).

According to paper, Model offers:

• Better accuracy than CNNs and Transformers
• Lower time complexity than CNNs and Transformers
• Lower parameters than CNNs and Transformers

Quick Start

Clone the repo and install the requirements.txt in a Python>=3.8 environment.

git clone https://github.com/Oguzhanercan/MLP-Mixer
cd MLP-Mixer
pip install -r requirements.txt

Dataset

There are 2 options for dataset. You can use pre-defined datasets listed below

• CIFAR10
• Mnist
• Fashion Mnist

or you can use your own dataset. Organize your folder structure as:

      data---
            |
            --0
               |
                --img0.png
                .
                .
                --img9999.png
            |
            -- 1
                |
                --img0.png
                .
                .
                --img9999.png
            .
            .

0 and 1 represents folders that contains images belongs only one particular class. There is no limit for classes or images.

Train

Open a terminal at the same directory of clone. Then run the code below.

python main.py --mode train --dataset CIFAR10 --save True --device cuda --epochs 20 --valid_per 0.2 

You can customize the model hyperparameters, all arguments listed below "Arguments:

• dataset
• train_path
• test_path
• batch_size
• im_size
• valid_per
• epochs
• learning_rate
• beta1
• beta2
• n_classes
• cuda
• -eveluate_per_epoch
• save_model
• model_path

Custom dataset mode should include following arguments: mode,dataset,train_path,n_classes,im_size