apple

/

TiC-CLIP-bestpool-cumulative

like 3

Hi,
Thanks for your interest. Here is an example for loading and evaluating the model:

import open_clip
from huggingface_hub import hf_hub_download
filename = hf_hub_download(repo_id="apple/TiC-CLIP-bestpool-cumulative", filename="checkpoints/2016.pt")
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', filename)

tokenizer = open_clip.get_tokenizer('ViT-B-16')

image = preprocess(Image.open("image.png").convert('RGB')).unsqueeze(0)
text = tokenizer(["a diagram", "a dog", "a cat"])

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)

    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:", text_probs)

Please note that these models are released to facilitate research on continual learning. Please refer to the model card for more code examples.