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---
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license: apache-2.0
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---
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## FDViT: Improve the Hierarchical Architecture of Vision Transformer (ICCV 2023)
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**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)
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Advanced Micro Devices, Inc.
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---
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## Dependancies
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```bash
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torch == 1.13.1
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torchvision == 0.14.1
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timm == 0.6.12
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einops == 0.6.1
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```
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## Model performance
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The image classification results of FDViT models on ImageNet dataset are shown in the following table.
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|Model|Parameters (M)|FLOPs(G)|Top-1 Accuracy (%)|
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|-|-|-|-|
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|FDViT-Ti|4.6|0.6|73.74|
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|FDViT-S|21.6|2.8|81.45|
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|FDViT-B|68.1|11.9|82.39|
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## Model Usage
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```bash
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from transformers import AutoModelForImageClassification
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import torch
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model = AutoModelForImageClassification.from_pretrained("FDViT_s", trust_remote_code=True)
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model.eval()
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inp = torch.ones(1,3,224,224)
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out = model(inp)
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```
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## Citation
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```
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@inproceedings{xu2023fdvit,
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title={FDViT: Improve the Hierarchical Architecture of Vision Transformer},
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author={Xu, Yixing and Li, Chao and Li, Dong and Sheng, Xiao and Jiang, Fan and Tian, Lu and Sirasao, Ashish},
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booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
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pages={5950--5960},
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year={2023}
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}
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```
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