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CeiT-pytorch

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Implementation of Convolutional enhanced image Transformer

// repository documentation

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# CeiT : Convolutional enhanced image Transformer
This is an unofficial PyTorch implementation of [Incorporating Convolution Designs into Visual Transformers](https://arxiv.org/abs/2103.11816) .
![](https://raw.githubusercontent.com/rishikksh20/CeiT-pytorch/master/assets/model1.PNG)
![](https://raw.githubusercontent.com/rishikksh20/CeiT-pytorch/master/assets/model2.PNG)

## Training :
```
python train.py -c configs/default.yaml --name "name_of_exp"
```
## Usage :
```python
import torch
from ceit import CeiT

img = torch.ones([1, 3, 224, 224])
    
model = CeiT(image_size = 224, patch_size = 4, num_classes = 100)
out = model(img)

print("Shape of out :", out.shape)      # [B, num_classes]

model = CeiT(image_size = 224, patch_size = 4, num_classes = 100, with_lca = True)
out = model(img)

print("Shape of out :", out.shape)      # [B, num_classes]

```

## Note :
* LCA might not be properly implemented.

## Citation :
```
@misc{yuan2021incorporating,
      title={Incorporating Convolution Designs into Visual Transformers}, 
      author={Kun Yuan and Shaopeng Guo and Ziwei Liu and Aojun Zhou and Fengwei Yu and Wei Wu},
      year={2021},
      eprint={2103.11816},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

## Acknowledgement :
* Base ViT code is borrowed from [@lucidrains](https://github.com/lucidrains) repo : https://github.com/lucidrains/vit-pytorch
* Training and dataloader code is borrowed from [@jeonsworld](https://github.com/jeonsworld) repo : https://github.com/jeonsworld/ViT-pytorch

CeiT-pytorch

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