This implementation from paper MLP Mixer. Give us a star if you like this repo.
Authors:
- Github: Xunino
- Email: ndlinh.ai@gmail.com
- Step 1:
conda create -n {your_env_name} python==3.7.0
- Step 2:
conda env create -f environment.yml
- Step 3:
conda activate {your_env_name}
-
Guide user how to download your data and set the data pipeline
-
Data pipeline example:
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
val/
...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
Script training:
python3 train.py --train-path={dataset/train} --val-path={dataset/val} --batch-size=32 --epochs=100 --n_blocks=8 --C=512 --DC=1024 --DS=256 --image-size=224 --patch-size=32 --augments=False --retrain=False