MLP-Mixer: An all-MLP Architecture for Vision, arxiv
PaddlePaddle training/validation code and pretrained models for MLP-Mixer.
The official TF implementation is here.
This implementation is developed by PaddleViT.
- Update (2022-03-30): Code is refactored.
- Update (2021-08-11): Model FLOPs and # params are uploaded.
- Update (2021-08-11): Code is released and ported weights are uploaded.
Model | Acc@1 | Acc@5 | #Params | FLOPs | Image Size | Crop_pct | Interpolation | Link |
---|---|---|---|---|---|---|---|---|
mlp_mixer_b16_224 | 76.60 | 92.23 | 60.0M | 12.7G | 224 | 0.875 | bicubic | google/baidu |
mlp_mixer_l16_224 | 72.06 | 87.67 | 208.2M | 44.9G | 224 | 0.875 | bicubic | google/baidu |
*The results are evaluated on ImageNet2012 validation set.
Note: MLP-Mixer weights are ported from timm
ImageNet2012 dataset is used in the following file structure:
│imagenet/
├──train_list.txt
├──val_list.txt
├──train/
│ ├── n01440764
│ │ ├── n01440764_10026.JPEG
│ │ ├── n01440764_10027.JPEG
│ │ ├── ......
│ ├── ......
├──val/
│ ├── n01440764
│ │ ├── ILSVRC2012_val_00000293.JPEG
│ │ ├── ILSVRC2012_val_00002138.JPEG
│ │ ├── ......
│ ├── ......
train_list.txt
: list of relative paths and labels of training images. You can download it from: google/baiduval_list.txt
: list of relative paths and labels of validation images. You can download it from: google/baidu
To use the model with pretrained weights, download the .pdparam
weight file and change related file paths in the following python scripts. The model config files are located in ./configs/
.
For example, assume weight file is downloaded in ./mixer_b16_224.pdparams
, to use the mixer_b16_224
model in python:
from config import get_config
from mlp_mixer import build_mlp_mixer as build_model
# config files in ./configs/
config = get_config('./configs/mixer_b16_224.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./mixer_b16_224.pdparams')
model.set_state_dict(model_state_dict)
To evaluate model performance on ImageNet2012, run the following script using command line:
sh run_eval_multi.sh
or
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/mixer_b16_224.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./mixer_b16_224.pdparams' \
-amp
Note: if you have only 1 GPU, change device number to
CUDA_VISIBLE_DEVICES=0
would run the evaluation on single GPU.
To train the model on ImageNet2012, run the following script using command line:
sh run_train_multi.sh
or
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/mixer_b16_224.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-amp
Note: it is highly recommanded to run the training using multiple GPUs / multi-node GPUs.
@article{tolstikhin2021mlp,
title={Mlp-mixer: An all-mlp architecture for vision},
author={Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner, Thomas and Yung, Jessica and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and others},
journal={arXiv preprint arXiv:2105.01601},
year={2021}
}