Update README.md

README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# LoRA-Ensemble: Uncertainty Modelling for Self-attention Networks
+Michelle Halbheer, Dominik J. Mühlematter, Alexander Becker, Dominik Narnhofer, Helge Aasen, Konrad Schindler and Mehmet Ozgur Turkoglu - 2024
+
+## Pretrained models
+This repository contains the pretrained models corresponding to the code we released on [GitHub](https://github.com/prs-eth/LoRA-Ensemble/).
+The usage of the models with our pipeline is described on GitHub.
+This repository only contains the models for our final experiments per dataset, not, however, for all intermediate results.
+
+## Citation
+If you find our work useful or interesting or use our code, please cite our paper as follows
+```latex
+@misc{
+  title = {LoRA-Ensemble: Uncertainty Modelling for Self-attention Networks},
+  author = {Halbheer, Michelle and M\"uhlematter, Dominik Jan and Becker, Alexander and Narnhofer, Dominik and Aasen, Helge and Schindler, Konrad and Turkoglu, Mehmet Ozgur}
+  year = {2024}
+  note = {arXiv: <arxiv code>}
+}
+```
+
+## CIFAR-100
+The table below shows the evaluation results obtained using different methods. Each method was trained five times with varying random seeds.
+
+| Method (ViT)              | Accuracy  | ECE   | Settings name*     | Model weights* |
+|----------------------|------------------------|-----------------------|-------------------|------------------------------|
+| Single Network       | \\(76.6\pm0.2\\)       | \\(0.144\pm0.001\\)   |CIFAR100_settings_explicit|Deep_Ensemble_ViT_base_32_1_members_CIFAR100_settings_explicit\<seed\>.pt|
+| Single Network with LoRA | \\(79.6\pm0.2\\)   | \\(\textbf{0.014}\pm0.003\\) |CIFAR100_settings_LoRA|LoRA_Former_ViT_base_32_1_members_CIFAR100_settings_LoRA\<seed\>.pt|
+| MC Dropout           | \\(77.1\pm0.5\\)       | \\(0.055\pm0.002\\)   |CIFAR100_settings_MCDropout|MCDropout_ViT_base_32_16_members_CIFAR100_settings_MCDropout\<seed\>.pt|
+| Explicit Ensemble    | \\(\underline{79.8}\pm0.2\\) | \\(0.098\pm0.001\\)     |CIFAR100_settings_explicit|Deep_Ensemble_ViT_base_32_16_members_CIFAR100_settings_explicit\<seed\>.pt|
+| LoRA-Ensemble        | \\(\textbf{82.5}\pm0.1\\) | \\(\underline{0.035}\pm0.001\\) |CIFAR100_settings_LoRA|LoRA_Former_ViT_base_32_16_members_CIFAR100_settings_LoRA\<seed\>.pt|
+
+\* Settings and model names are followed by a number in the range 1-5 indicating the used random seed.
+
+## HAM10000
+The table below shows the evaluation results obtained using different methods. Each method was trained five times with varying random seeds.
+| Method (ViT)               | Accuracy| ECE  | Settings name*     | Model weights* |
+|----------------------|------------------------|-----------------------|-------------------|------------------------------|
+| Single Network       | \\(84.3\pm0.5\\)       | \\(0.136\pm0.006\\)   |HAM10000_settings_explicit|Deep_Ensemble_ViT_base_32_1_members_HAM10000_settings_explicit\<seed\>.pt|
+| Single Network with LoRA | \\(83.2\pm0.7\\)      | \\(0.085\pm0.004\\)       |HAM10000_settings_LoRA|LoRA_Former_ViT_base_32_1_members_HAM10000_settings_LoRA\<seed\>.pt|
+| MC Dropout           | \\(83.7\pm0.4\\)           | \\(\underline{0.099}\pm0.007\\) |HAM10000_settings_MCDropout|MCDropout_ViT_base_32_16_members_HAM10000_settings_MCDropout\<seed\>.pt|
+| Explicit Ensemble    | \\(\underline{85.7}\pm0.3\\) | \\(0.106\pm0.002\\)     |HAM10000_settings_explicit|Deep_Ensemble_ViT_base_32_16_members_HAM10000_settings_explicit\<seed\>.pt|
+| LoRA-Ensemble        | \\(\textbf{88.0}\pm0.2\\) | \\(\textbf{0.037}\pm0.002\\) |HAM10000_settings_LoRA|LoRA_Former_ViT_base_32_16_members_HAM10000_settings_LoRA\<seed\>.pt|
+
+\* Settings and model names are followed by a number in the range 1-5 indicating the used random seed.