Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision

MLP Mixer

Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision. Give us a star if you like this repo.

Author:

• Github: bangoc123
• Email: [email protected]

This library belongs to our project: Papers-Videos-Code where we will implement AI SOTA papers and publish all source code. Additionally, videos to explain these models will be uploaded to ProtonX Youtube channels.

[Note] You can use your data to train this model.

I. Set up environment

1. Make sure you have installed Miniconda. If not yet, see the setup document here.

2. cd into mlp-mixer and use command line conda env create -f environment.yml to setup the environment

3. Run conda environment using the command conda activate mlp-mixer

II. Set up your dataset.

Create 2 folders train and validation in the data folder (which was created already). Then Please copy your images with the corresponding names into these folders.

• train folder was used for the training process
• validation folder was used for validating training result after each epoch

This library use image_dataset_from_directory API from Tensorflow 2.0 to load images. Make sure you have some understanding of how it works via its document.

Structure of these folders.

train/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
...class_c/
......c_image_1.jpg
......c_image_2.jpg

validation/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
...class_c/
......c_image_1.jpg
......c_image_2.jpg

III. Train your model by running this command line

python train.py --epochs ${epochs} --num-classes ${num_classes}

You want to train a model in 10 epochs for binary classification problems (with 2 classes)

Example:

python train.py --epochs 10 --num-classes 2

There are some important arguments for the script you should consider when running it:

• train-folder: The folder of training images
• valid-folder: The folder of validation images
• model-folder: Where the model after training saved
• num-classes: The number of your problem classes.
• batch-size: The batch size of the dataset
• c: Patch Projection Dimension
• dc: Token-mixing units. It was mentioned in the paper on page 3
• ds: Channel-mixing units. It was mentioned in the paper on page 3
• num-of-mlp-blocks: The number of MLP Blocks
• learning-rate: The learning rate of Adam Optimizer

After training successfully, your model will be saved to model-folder defined before

IV. Testing model with a new image

We offer a script for testing a model using a new image via a command line:

python predict.py --test-file-path ${test_file_path}

where test_file_path is the path of your test image.

Example:

python predict.py --test-file-path ./data/test/cat.2000.jpg

V. Feedback

If you meet any issues when using this library, please let us know via the issues submission tab.