timm/tiny_vit_21m_224.dist_in22k_ft_in1k

from urllib.request import urlopen
from PIL import Image
import timm

# Clasificación de imágenes
img = Image.open(urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model('tiny_vit_21m_224.dist_in22k_ft_in1k', pretrained=True)
model = model.eval()
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0))
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

# Extracción del mapa de características
model = timm.create_model('tiny_vit_21m_224.dist_in22k_ft_in1k', pretrained=True, features_only=True)
model = model.eval()
output = model(transforms(img).unsqueeze(0))
for o in output:
    print(o.shape)

# Embeber imágenes
model = timm.create_model('tiny_vit_21m_224.dist_in22k_ft_in1k', pretrained=True, num_classes=0)
model = model.eval()
output = model.forward_features(transforms(img).unsqueeze(0))
output = model.forward_head(output, pre_logits=True)