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---
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.
---
## 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_ti", 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}
}
```
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