SimMIM

By Zhenda Xie*, Zheng Zhang*, Yue Cao*, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai and Han Hu*.

This repo is the official implementation of "SimMIM: A Simple Framework for Masked Image Modeling".

Updates

12/09/2021

Initial commits:

Pre-trained and fine-tuned models on ImageNet-1K (Swin Base, Swin Large, and ViT Base) are provided.
The supported code for ImageNet-1K pre-training and fine-tuneing is provided.

Introduction

SimMIM is initially described in arxiv, which serves as a simple framework for masked image modeling. From systematically study, we find that simple designs of each component have revealed very strong representation learning performance: 1) random masking of the input image with a moderately large masked patch size (e.g., 32) makes a strong pre-text task; 2) predicting raw pixels of RGB values by direct regression performs no worse than the patch classification approaches with complex designs; 3) the prediction head can be as light as a linear layer, with no worse performance than heavier ones.

Main Results on ImageNet

Swin Transformer

ImageNet-1K Pre-trained and Fine-tuned Models

name	pre-train epochs	pre-train resolution	fine-tune resolution	acc@1	pre-trained model	fine-tuned model
Swin-Base	100	192x192	192x192	82.8	google/config	google/config
Swin-Base	100	192x192	224x224	83.5	google/config	google/config
Swin-Base	800	192x192	224x224	84.0	google/config	google/config
Swin-Large	800	192x192	224x224	85.4	google/config	google/config
SwinV2-Huge	800	192x192	224x224	85.7	/	/
SwinV2-Huge	800	192x192	512x512	87.1	/	/

Vision Transformer

ImageNet-1K Pre-trained and Fine-tuned Models

name	pre-train epochs	pre-train resolution	fine-tune resolution	acc@1	pre-trained model	fine-tuned model
ViT-Base	800	224x224	224x224	83.8	google/config	google/config

Citing SimMIM

@article{xie2021simmim,
  title={SimMIM: A Simple Framework for Masked Image Modeling},
  author={Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Bao, Jianmin and Yao, Zhuliang and Dai, Qi and Hu, Han},
  journal={arXiv preprint arXiv:2111.09886},
  year={2021}
}

Getting Started

Installation

Install CUDA 11.3 with cuDNN 8 following the official installation guide of CUDA and cuDNN.
Setup conda environment:

# Create environment
conda create -n SimMIM python=3.8 -y
conda activate SimMIM

# Install requirements
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch -y

# Install apex
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
cd ..

# Clone SimMIM
git clone https://github.com/microsoft/SimMIM
cd SimMIM

# Install other requirements
pip install -r requirements.txt

Evaluating provided models

To evaluate a provided model on ImageNet validation set, run:

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_finetune.py \
--eval --cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>

For example, to evaluate the Swin Base model on a single GPU, run:

python -m torch.distributed.launch --nproc_per_node 1 main_finetune.py \
--eval --cfg configs/swin_base__800ep/simmim_finetune__swin_base__img224_window7__800ep.yaml --resume simmim_finetune__swin_base__img224_window7__800ep.pth --data-path <imagenet-path>

Pre-training with SimMIM

To pre-train models with SimMIM, run:

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_simmim.py \ 
--cfg <config-file> --data-path <imagenet-path>/train [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]

For example, to pre-train Swin Base for 800 epochs on one DGX-2 server, run:

python -m torch.distributed.launch --nproc_per_node 16 main_simmim.py \ 
--cfg configs/swin_base__800ep/simmim_pretrain__swin_base__img192_window6__800ep.yaml --batch-size 128 --data-path <imagenet-path>/train [--output <output-directory> --tag <job-tag>]

Fine-tuning pre-trained models

To fine-tune models pre-trained by SimMIM, run:

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> main_finetune.py \ 
--cfg <config-file> --data-path <imagenet-path> --pretrained <pretrained-ckpt> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]

For example, to fine-tune Swin Base pre-trained by SimMIM on one DGX-2 server, run:

python -m torch.distributed.launch --nproc_per_node 16 main_finetune.py \ 
--cfg configs/swin_base__800ep/simmim_finetune__swin_base__img224_window7__800ep.yaml --batch-size 128 --data-path <imagenet-path> --pretrained <pretrained-ckpt> [--output <output-directory> --tag <job-tag>]

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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