---
license: apache-2.0
---
## FDViT: Improve the Hierarchical Architecture of Vision Transformer (ICCV 2023)

**Yixing Xu, Chao Li, Dong Li, Xiao Sheng, Fan Jiang, Lu Tian, Ashish Sirasao** | [Paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Xu_FDViT_Improve_the_Hierarchical_Architecture_of_Vision_Transformer_ICCV_2023_paper.pdf)

Advanced Micro Devices, Inc.

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## Dependancies

```bash
torch == 1.13.1
torchvision == 0.14.1
timm == 0.6.12
einops == 0.6.1
```

## Model performance

The image classification results of FDViT models on ImageNet dataset are shown in the following table.

|Model|Parameters (M)|FLOPs(G)|Top-1 Accuracy (%)|
|-|-|-|-|
|FDViT-Ti|4.6|0.6|73.74|
|FDViT-S|21.6|2.8|81.45|
|FDViT-B|68.1|11.9|82.39|

## Model Usage

```bash
from transformers import AutoModelForImageClassification
import torch

model = AutoModelForImageClassification.from_pretrained("FDViT_b", trust_remote_code=True)

model.eval()

inp = torch.ones(1,3,224,224)
out = model(inp)
```

## Citation

```
@inproceedings{xu2023fdvit,
  title={FDViT: Improve the Hierarchical Architecture of Vision Transformer},
  author={Xu, Yixing and Li, Chao and Li, Dong and Sheng, Xiao and Jiang, Fan and Tian, Lu and Sirasao, Ashish},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5950--5960},
  year={2023}
}
```