You can deploy this model and start earning money today!

timm/
csatv2_21m.sw_r640_in1k

Name: csatv2_21m.sw_r640_in1k
Availability: InStock

Views

No views yet

Model card for csatv2_21m.sw_r640_in1k

A CSATv2 image classification model pretrained with timm on ImageNet-1k by Ross Wightman.

Model Details

Model Type: Image Classification / Feature Encoder
Model Stats:
- Params (M): 20.7
- GMACs: 4.7
- Activations (M): 26.7
- Image size: 640 x 640
Original: https://huggingface.co/Hyunil/CSATv2
License: apache-2.0
Dataset: ImageNet-1k
Papers:

Model Usage

Image Classification

python

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('csatv2_21m.sw_r640_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
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 single image into batch of 1

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

Feature Map Extraction

python

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(
    'csatv2_21m.sw_r640_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
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 single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 48, 80, 80])
    #  torch.Size([1, 96, 40, 40])
    #  torch.Size([1, 224, 20, 20])
    #  torch.Size([1, 448, 10, 10])

    print(o.shape)

Image Embeddings

python

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(
    'csatv2_21m.sw_r640_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
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, 448, 10, 10) shaped tensor

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

timm/csatv2_21m.sw_r640_in1k

Model card for csatv2_21m.sw_r640_in1k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Citation

timm/
csatv2_21m.sw_r640_in1k