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To visualize the features of the ViT models:

PYTHONPATH=. python experiments/it15/vis35.py -l <layer_number> -f <feature_number> -n  <network_number> -v <tv_coefficient>

For example:

PYTHONPATH=. python experiments/it15/vis35.py -l 4 -f 20 -n  35 -v 0.1

To visualize the features of the CLIP models:

PYTHONPATH=. python experiments/it15/vis98.py -l <layer_number> -f <feature_number> -n  <network_number> -v <tv_coefficient>

For example:

PYTHONPATH=. python experiments/it15/vis98.py -l 4 -f 20 -n  98 -v 0.1

For the ViT models the -n option should be in [34, 35, 36, 37, 38, 39], and for the CLIP models the -n option should be in [94, 95, 96, 97, 98, 99]

To list all the available network numbers use:

python show_models.py

Here we list some of them:

• 34: ViT0_B_16_imagenet1k
• 35: ViT1_B_32_imagenet1k
• 36: ViT2_L_16_imagenet1k
• 37: ViT3_L_32_imagenet1k
• 38: ViT4_B_16
• 39: ViT5_B_32
• 94: CLIP0_RN50
• 95: CLIP1_RN101
• 96: CLIP2_RN50x4
• 97: CLIP3_RN50x16
• 98: CLIP4_ViT-B/32
• 99: CLIP5_ViT-B/16

We use the timm library to load the pretrained models. After running these commands, you can find the visualizations in the desktop folder.

Other experiments done in the paper can be found in the experiments folder.

For the experiments that we need to load the imagenet dataset like the isolating CLS experiment, the code assumes that the dataset is in data/imagenet/train for the training set, and data/imagenet/val for the validation set.

We will update the readme with more instructions on how to run other experiments soon.