### To reproduce results of synthetic datasets

`cd baseline-SC`

`bash main-SC.sh`



### To reproduce results of binary classification tasks

`cd baseline-BC`

`bash main-BC.sh`



### To reproduce results of visual recognition tasks

To apply ST$_k$, we made some modifications to the following paths within the PEL framework: `./PEL/trainer`, `./PEL/utils/losses.py`, and `./PEL/configs/{clip_vit_b16_peft.yaml, in21k_vit_b16_peft.yaml}`.

`cd PEL`

`python main.py -d cifar100_ir100 -m in21k_vit_b16_peft`

download ImageNet-1K and Places as:


Path/To/Places
├─ train
│  ├─ airfield
|  |  ├─ 00000001.jpg
|  |  └─ ......
│  └─ ......
└─ val
   ├─ airfield
   |  ├─ Places365_val_00000435.jpg
   |  └─ ......
   └─ ......

Path/To/ImageNet-1K
├─ train
│  ├─ n01440764
|  |  ├─ n01440764_18.JPEG
|  |  └─ ......
│  └─ ......
└─ val
   ├─ n01440764
   |  ├─ ILSVRC2012_val_00000293.JPEG
   |  └─ ......
   └─ ......

`python main.py -d places_lt -m in21k_vit_b16_peft`

`python main.py -d imagenet_lt -m in21k_vit_b16_peft`
