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ConvMLP: Hierarchical Convolutional MLPs for Vision, arxiv

PaddlePaddle training/validation code and pretrained models for ConvMLP.

The official and 3rd party pytorch implementation are here.

This implementation is developed by PPViT.

drawing

ViP Model Overview

Update

Update (2022-04-08): Code is uploaded. Update (2021-09-26): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
convmlp_s 76.76 93.40 9.0M 2.4G 224 0.875 bicubic google/baidu
convmlp_m 79.03 94.53 17.4M 4.0G 224 0.875 bicubic google/baidu
convmlp_l 80.15 95.00 42.7M 10.0G 224 0.875 bicubic google/baidu

*The results are evaluated on ImageNet2012 validation set.

Note: ConvMLP weights are ported from here

Data Preparation

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/baidu
  • val_list.txt: list of relative paths and labels of validation images. You can download it from: google/baidu

Usage

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 ./convmlp_s.pdparams, to use the convmlp_s model in python:

from config import get_config
from convmlp import build_convmlp as build_model
# config files in ./configs/
config = get_config('./configs/convmlp_s.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./convmlp_s.pdparams')
model.set_state_dict(model_state_dict)

Evaluation

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/convmlp_s.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./convmlp_s.pdparams' \
-amp

Note: if you have only 1 GPU, change device number to CUDA_VISIBLE_DEVICES=0 would run the evaluation on single GPU.

Training

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/convmlp_s.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.

Reference

@article{li2021convmlp,
      title={ConvMLP: Hierarchical Convolutional MLPs for Vision}, 
      author={Jiachen Li and Ali Hassani and Steven Walton and Humphrey Shi},
      year={2021},
      eprint={2109.04454},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}