Main Results on ImageNet with Pretrained Models

Last update: Dec 14, 2022

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Overview

This repository contains Pytorch evaluation code, training code and pretrained models for the following projects:

SPACH (A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP)
sMLP (Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?)
ShiftViT (When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism)

Main Results on ImageNet with Pretrained Models

name	[email protected]	#params	FLOPs	url
SPACH-Conv-MS-S	81.6	44M	7.2G	github
SPACH-Trans-MS-S	82.9	40M	7.6G	github
SPACH-MLP-MS-S	82.1	46M	8.2G	github
SPACH-Hybrid-MS-S	83.7	63M	11.2G	github
SPACH-Hybrid-MS-S+	83.9	63M	12.3G	github
sMLPNet-T	81.9	24M	5.0G
sMLPNet-S	83.1	49M	10.3G	github
sMLPNet-B	83.4	66M	14.0G	github
Shift-T / light	79.4	20M	3.0G	github
Shift-T	81.7	29M	4.5G	github
Shift-S / light	81.6	34M	5.7G	github
Shift-S	82.8	50M	8.8G	github

Usage

Install

First, clone the repo and install requirements:

git clone https://github.com/microsoft/Spach
pip install -r requirements.txt

Data preparation

Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val/ folder respectively:

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class/2
      img4.jpeg

Evaluation

To evaluate a pre-trained model on ImageNet val with a single GPU run:

python main.py --eval --resume <checkpoint> --model <model-name>--data-path <imagenet-path>

For example, to evaluate the SPACH-Hybrid-MS-S model, run

python main.py --eval --resume --model spach_ms_s_patch4_224_hybrid spach_ms_hybrid_s.pth --data-path <imagenet-path>

giving

* [email protected] 83.658 [email protected] 96.762 loss 0.688

You can find all supported models in models/registry.py.

Training

One can simply call the following script to run training process. Distributed training is recommended even on single GPU node.

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --use_env main.py \
--model <model-name>
--data-path <imagenet-path>
--output_dir <output-path>
--dist-eval

Citation

@article{zhao2021battle,
  title={A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP},
  author={Zhao, Yucheng and Wang, Guangting and Tang, Chuanxin and Luo, Chong and Zeng, Wenjun and Zha, Zheng-Jun},
  journal={arXiv preprint arXiv:2108.13002},
  year={2021}
}

@article{tang2021sparse,
  title={Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?},
  author={Tang, Chuanxin and Zhao, Yucheng and Wang, Guangting and Luo, Chong and Xie, Wenxuan and Zeng, Wenjun},
  journal={arXiv preprint arXiv:2109.05422},
  year={2021}
}

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 [email protected] with any additional questions or comments.

Acknowledgement

Our code are built on top of DeiT. We test throughput following Swin Transformer

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Comments

Shift features implementation

Hi, very interesting research. I wonder why did you implement the shift_feature as memory copy https://github.com/microsoft/SPACH/blob/497c1d86fffd9d48e26c0484fb845ff04c328cca/models/shiftvit.py#L107 instead of using Tensor.roll operation? It would make your block much faster. Another benefit would be that pixels from one side would leak to the other giving the network to pass information from one boundary to another, which seems a better option that dublication of the last row during each shift.

opened by bonlime 3
Add: unofficial implementation

Hey folks,

It would be great if this repository could also hold links for other unofficial implementations. I am proposing a keras tutorial on ShiftViT.

opened by ariG23498 0
The configuration of the architecture variants is inconsistent with the papers and weights files.

@tangchuanxin

https://github.com/microsoft/SPACH/blob/497c1d86fffd9d48e26c0484fb845ff04c328cca/models/registry.py#L224

The code is inconsistent with the content of the paper:

and the weight file. The content of this pth file is the same as the architecture variant -S in the figure above, ie, depths=(6, 8, 18, 6).

https://github.com/microsoft/SPACH/releases/download/v1.0/shiftvit_tiny_r2.pth

opened by lartpang 1

Main Results on ImageNet with Pretrained Models

Related tags

Overview

Main Results on ImageNet with Pretrained Models

Usage

Install

Data preparation

Evaluation

Training

Citation

Contributing

Acknowledgement

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Comments

Shift features implementation

Add: unofficial implementation

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Releases(v1.0)

v1.0(Nov 19, 2021)

Owner

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