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license: apache-2.0 |
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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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|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_ti", 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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