efficientformer l1.snap dist in1k EU AI Act Compliance Profile

Model card for efficientformer_l1.snap_dist_in1k

A EfficientFormer image classification model. Pretrained with distillation on ImageNet-1k.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 12.3
  • GMACs: 1.3
  • Activations (M): 5.5
  • Image size: 224 x 224
• Original: https://github.com/snap-research/EfficientFormer
• Papers:
  • EfficientFormer: Vision Transformers at MobileNet Speed: https://arxiv.org/abs/2206.01191
• Dataset: ImageNet-1k

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('efficientformer_l1.snap_dist_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)

Image Embeddings

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(
    'efficientformer_l1.snap_dist_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)

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