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1from PIL import Image
2import torch
3import timm
4from timm.data import create_transform
5from timm.data import resolve_model_data_config
6
7model = timm.create_model("hf-hub:hassonofer/vit_so150m_patch14_reg4_biodino_252", pretrained=True)
8model.eval()
9
10data_config = resolve_model_data_config(model)
11transform = create_transform(**data_config, is_training=False)
12
13image = Image.open("path/to/image.jpg").convert("RGB")
14x = transform(image).unsqueeze(0)
15
16with torch.inference_mode():
17 embedding = model(x)
18
19print(embedding.shape) # torch.Size([1, 896])