Update README.md

README.md

@@ -1,3 +1,57 @@
----
-license: apache-2.0
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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)
+# print(out.logits)
+print(torch.sum(out.logits))
+```
+
+## 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}
+}
+```