First commit

README.md

@@ -1 +1,59 @@
-# visual_prompting
+# Exploring Visual Prompts for Adapting Large-Scale Models
+
+[[Paper]](https://arxiv.org/abs/2203.17274) [[Project Page]](https://hjbahng.github.io/visual_prompting/)
+
+## Overview
+We investigate the efficacy of visual prompting, a novel way of adapting pre-trained vision models to downstream tasks by only modifying the pixel space. You can find more details in our [paper](https://arxiv.org/abs/2203.17274).
+
+![](./figures/clip_vs_vision.png)
+
+
+## Installation
+Clone this repo:
+```bash
+git clone https://github.com/hjbahng/visual_prompting.git
+cd visual_prompting
+```
+
+This code requires python 3+. Install dependencies by:
+```bash
+pip install -r requirements.txt
+```
+
+Prepare the pre-trained models:
+```bash
+bash models/download_models.sh
+```
+
+## Training
+* Training for CLIP:
+```bash
+python main_clip.py --dataset cifar100 --root [path_to_cifar100] 
+```
+
+* Training for vision models:
+```bash
+python main_vision.py --model bit_m --dataset cifar100 --root [path_to_cifar100]
+```
+## Testing
+* Testing for CLIP:
+```bash
+python main_clip.py --evaluate --resume /path/to/checkpoints/model_best.pth.tar --dataset cifar100 --root [path_to_cifar100]
+```
+
+* Testing for vision models:
+```bash
+python main_vision.py --evaluate --resume /path/to/checkpoints/model_best.pth.tar --model bit_m --dataset cifar100 --root [path_to_cifar100]
+```
+
+
+## Citation
+If you use this code for your research, please cite our paper.
+```
+@article{bahng2022visual,
+         title={Exploring Visual Prompts for Adapting Large-Scale Models}, 
+         author={Hyojin Bahng and Ali Jahanian and Swami Sankaranarayanan and Phillip Isola},
+         journal={arXiv preprint arXiv:2203.17274},
+         year={2022}
+}
+```