have you tried different CLIP models? about clip_prefix_caption HOT 4 OPEN

I tried ViT-L/14. You have to just change it in inference code and feature extractor code. For example parse_coco.py:

parser.add_argument('--clip_model_type', default="ViT-L/14", choices=('RN50', 'RN101', 'RN50x4', 'ViT-B/32', 'ViT-L/14'))

Just add argument choice. But in train code we need to change prefix_dim. It is 768 for ViT-L/14

Hi there, would you mind sharing your ViT/L-14 model checkpoints?
Thanks.

from clip_prefix_caption.

Have you tried it? I just have same question.

from clip_prefix_caption.

I tried ViT-L/14. You have to just change it in inference code and feature extractor code.
For example parse_coco.py:

parser.add_argument('--clip_model_type', default="ViT-L/14", choices=('RN50', 'RN101', 'RN50x4', 'ViT-B/32', 'ViT-L/14'))

Just add argument choice.
But in train code we need to change prefix_dim. It is 768 for ViT-L/14

from clip_prefix_caption.

I tried ViT-L/14. You have to just change it in inference code and feature extractor code. For example parse_coco.py:

parser.add_argument('--clip_model_type', default="ViT-L/14", choices=('RN50', 'RN101', 'RN50x4', 'ViT-B/32', 'ViT-L/14'))

Just add argument choice. But in train code we need to change prefix_dim. It is 768 for ViT-L/14

Hi there, would you mind sharing your ViT/L-14 model checkpoints? Thanks.

I would be very interested in having access to the checkpoints too! 😊

from clip_prefix_caption.