Create README.md

README.md

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+# RobustVLM (Foundation Models) via Object-centric Learning
+
+
+## Table of Contents
+- [Installation](#installation)
+- [Stage1: Get Object-centric Models](#models)
+    - [Dataset](#loading-pretrained-models)
+    - [Training](#summary-of-results)
+
+## Installation
+Create and activate anaconda environment:
+```shell
+conda create -n robustclip python==3.11
+```
+```shell
+conda activate robustclip 
+```
+
+The code is tested with Python 3.11. To install the required packages, run:
+```shell
+pip install -r requirements.txt
+```
+
+To install the open_clip_torch locally run:
+```shell
+cd ./open_clip_torch
+```
+```shell
+python setup.py develop
+```
+
+## Stage1: Get Object-centric Models
+
+### Dataset
+Prepare the ImageNet dataset in a torch.ImageFolder style format:
+```
+dataset_path
+└─imagenet
+  └─train
+    └─n01440764
+        xxxxxx.JPEG
+        .....
+    └─......
+  └─val
+    └─n04254680
+        xxxxxx.JPEG
+        .....
+    └─......
+```
+### Training
+- Slot-Attention on 4GPUs
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m train.training_clip_slots --clip_model_name ViT-L-14 --pretrained openai --dataset imagenet --imagenet_root /.../.../dataset_path/imagenet --template std --output_normalize False --steps 1000000 --warmup 10000 --batch_size 128 --loss l2 --opt adamw --lr 5e-5 --wd 1e-4 --attack pgd --inner_loss l2 --norm linf --eps 4 --iterations_adv 10 --stepsize_adv 1 --wandb False --output_dir ./output_slots --experiment_name SLOTS --log_freq 1000 --eval_freq 1000```
+```
+
+The results of reconstruction after slot-attention and ckps are stored in './output_slots/ViT-L-14_openai_imagenet_l2_imagenet_SLOTS_xxxxx'
+