timm/tf_efficientnetv2_b3.in21k

Clasificación de imágenes

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

img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('tf_efficientnetv2_b3.in21k', pretrained=True)
model = model.eval()

# obtener las transformaciones específicas del modelo (normalización, redimensionamiento)
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)) # unsqueeze una sola imagen en un batch de 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Extracción del Mapa de Características

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

img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
'tf_efficientnetv2_b3.in21k',
pretrained=True,
features_only=True,
)
model = model.eval()

# obtener las transformaciones específicas del modelo (normalización, redimensionamiento)
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)) # unsqueeze una sola imagen en un batch de 1

for o in output:
# print shape of each feature map in output
# e.g.:
#  torch.Size([1, 16, 120, 120])
#  torch.Size([1, 40, 60, 60])
#  torch.Size([1, 56, 30, 30])
#  torch.Size([1, 136, 15, 15])
#  torch.Size([1, 232, 8, 8])

print(o.shape)

Embeddings de imágenes

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

img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
'tf_efficientnetv2_b3.in21k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()

# obtener las transformaciones específicas del modelo (normalización, redimensionamiento)
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)) # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1536, 8, 8) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor