Model card for tf_efficientnetv2_s.in21k_ft_in1k

A EfficientNet-v2 image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 21.5
  • GMACs: 5.4
  • Activations (M): 22.7
  • Image size: train = 300 x 300, test = 384 x 384
• Papers:
  • EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298
• Dataset: ImageNet-1k
• Pretrain Dataset: ImageNet-21k
• Original: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

Model Usage

Image Classification

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_s.in21k_ft_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

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_s.in21k_ft_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, 24, 150, 150])
    #  torch.Size([1, 48, 75, 75])
    #  torch.Size([1, 64, 38, 38])
    #  torch.Size([1, 160, 19, 19])
    #  torch.Size([1, 256, 10, 10])

    print(o.shape)