fix readme

README.md

@@ -1,124 +1,11 @@
-# RobustViT
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hila-chefer/RobustViT/blob/master/RobustViT.ipynb)
-
-Official PyTorch implementation of **Optimizing Relevance Maps of Vision Transformers Improves Robustness**. This code allows to 
-finetune the explainability maps of Vision Transformers to enhance robustness.
-
-The method employs loss functions directly to the explainability maps to ensure that the model is focused mostly on the foreground of the image:
-<p align="center">
-  <img width="500" height="400" src="teaser.png">
-</p>
-Using a short finetuning process with only 3 labeled examples from 500 classes, our method imrpoves robustness of ViT models across different model sizes and training techniques, even when data augmentations/ regularization are applied.
-
-## Producing Segmenataion Data
-### Using ImageNet-S
-To use the ImageNet-S labeled data, [download the `ImageNetS919` dataset](https://github.com/UnsupervisedSemanticSegmentation/ImageNet-S)
-
-### Using TokenCut for unsupervised segmentation
-1.  Clone the TokenCut project
-    ```
-    git clone https://github.com/YangtaoWANG95/TokenCut.git
-    ```
-2.  Install the dependencies
-    Python 3.7, PyTorch 1.7.1 and CUDA 11.2. Please refer to the official installation. If CUDA 10.2 has been properly installed:
-    ```
-    pip install torch==1.7.1 torchvision==0.8.2
-    ```
-    Followed by
-    ```
-    pip install -r TokenCut/requirements.txt
-    
-3. Use the following command to extract the segmentation maps:
-    ```
-   python tokencut_generate_segmentation.py --img_path <PATH_TO_IMAGE> --out_dir <PATH_TO_OUTPUT_DIRECTORY>    
-   ```
-
-
-## Finetuning ViT models
-
-To finetune a pretrained ViT model use the `imagenet_finetune.py` script. Notice to uncomment the import line containing the pretrained model you 
-wish to finetune.
-
-Usage example:
-
-```bash
-python imagenet_finetune.py --seg_data <PATH_TO_SEGMENTATION_DATA> --data <PATH_TO_IMAGENET> --gpu 0  --lr <LR> --lambda_seg <SEG> --lambda_acc <ACC> --lambda_background <BACK> --lambda_foreground <FORE>
-```
-
-Notes:
-
-* For all models we use :
-    * `lambda_seg=0.8`
-    * `lambda_acc=0.2`
-    * `lambda_background=2`
-    * `lambda_foreground=0.3`
- * For **DeiT** models, a temprature is required as follows:
-    * `temprature=0.65` for DeiT-B
-    * `temprature=0.55` for DeiT-S
- * The learning rates per model are:
-    * ViT-B: 3e-6
-    * ViT-L: 9e-7
-    * AR-S: 2e-6
-    * AR-B: 6e-7
-    * AR-L: 9e-7
-    * DeiT-S: 1e-6
-    * DeiT-B: 8e-7
-
-## Baseline methods
-Notice to uncomment the import line containing the pretrained model you wish to finetune in the code.
-
-### GradMask
-Run the following command: 
-```bash
-python imagenet_finetune_gradmask.py --seg_data <PATH_TO_SEGMENTATION_DATA> --data <PATH_TO_IMAGENET> --gpu 0  --lr <LR> --lambda_seg <SEG> --lambda_acc <ACC>
-```
-All hyperparameters for the different models can be found in section D of the supplementary material.
-
-### Right for the Right Reasons
-Run the following command: 
-```bash
-python imagenet_finetune_rrr.py --seg_data <PATH_TO_SEGMENTATION_DATA> --data <PATH_TO_IMAGENET> --gpu 0  --lr <LR> --lambda_seg <SEG> --lambda_acc <ACC>
-```
-All hyperparameters for the different models can be found in section D of the supplementary material.
-
-## Evaluation
-
-### Robustness Evaluation
-
-1. Download the evaluation datasets: 
-    * [INet-A](https://github.com/hendrycks/natural-adv-examples)
-    * [INet-R](https://github.com/hendrycks/imagenet-r)
-    * [INet-v2](https://github.com/modestyachts/ImageNetV2)
-    * [ObjectNet](https://objectnet.dev/)
-    * [SI-Score](https://github.com/google-research/si-score)
-
-2. Run the following script to evaluate:
- 
-```bash
-python imagenet_eval_robustness.py --data <PATH_TO_ROBUSTNESS_DATASET> --batch-size <BATCH_SIZE> --evaluate --checkpoint <PATH_TO_FINETUNED_CHECKPOINT>
-```
-* Notice to uncomment the import line containing the pretrained model you wish to evaluate in the code.
-* To evaluate the original model simply omit the `checkpoint` parameter.
-* For the INet-v2 dataset add `--isV2`.
-* For the ObjectNet dataset add `--isObjectNet`.
-* For the SI datasets add `--isSI`.
-
-### Segmentation Evaluation
-Our segmentation tests are based on the test in the official implementation of [Transformer Interpretability Beyond Attention Visualization](https://github.com/hila-chefer/Transformer-Explainability).
-1. [Download the ImageNet segmentation test set](https://github.com/hila-chefer/Transformer-Explainability#section-a-segmentation-results).
-2. Run the following script to evaluate:
- 
- ```bash
-PYTHONPATH=./:$PYTHONPATH python SegmentationTest/imagenet_seg_eval.py  --imagenet-seg-path <PATH_TO_gtsegs_ijcv.mat>
-```
-* Notice to uncomment the import line containing the pretrained model you wish to evaluate in the code.
-
-### Credits
-* The TokenCut code is built on top of [LOST](https://github.com/valeoai/LOST), [DINO](https://github.com/facebookresearch/dino), [Segswap](https://github.com/XiSHEN0220/SegSwap), and [Bilateral_Sovlver](https://github.com/poolio/bilateral_solver). 
-* Our ViT code is based on the [pytorch-image-models](https://github.com/rwightman/pytorch-image-models) repository.
-* Our ImageNet finetuning code is based on [code from the official PyTorch repo](https://github.com/pytorch/examples/blob/main/imagenet/main.py).
-* The code to convert ObjectNet classes to ImageNet classes was taken from [the torchprune repo](https://github.com/lucaslie/torchprune/blob/b753745b773c3ed259bf819d193ce8573d89efbb/src/torchprune/torchprune/util/datasets/objectnet.py).
-* The code to convert SI-Score classes to ImageNet classes was taken from [the official implementation](https://github.com/google-research/si-score).
-
-We would like to sincerely thank the authors for their great works. 
+---
+title: RobustViT
+emoji: ⚡
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.0.11
+app_file: app.py
+pinned: false
+license: mit
+---