| --- |
| license: apache-2.0 |
| --- |
| ## 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 |
|
|
| ```bash |
| torch == 1.13.1 |
| torchvision == 0.14.1 |
| timm == 0.6.12 |
| einops == 0.6.1 |
| ``` |
|
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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 (%)| |
| |-|-|-|-| |
| |FDViT-Ti|4.6|0.6|73.74| |
| |FDViT-S|21.6|2.8|81.45| |
| |FDViT-B|68.1|11.9|82.39| |
|
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| ## Model Usage |
|
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| ```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 |
|
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| ``` |
| @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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