eca nfnet l0.ra2 in1k EU AI Act Compliance Profile

Model card for eca_nfnet_l0.ra2_in1k

A ECA-NFNet-Lite (Lightweight NFNet w/ ECA attention) image classification model. Trained in timm by Ross Wightman.

Normalization Free Networks are (pre-activation) ResNet-like models without any normalization layers. Instead of Batch Normalization or alternatives, they use Scaled Weight Standardization and specifically placed scalar gains in residual path and at non-linearities based on signal propagation analysis.

Lightweight NFNets are timm specific variants that reduce the SE and bottleneck ratio from 0.5 -> 0.25 (reducing widths) and use a smaller group size while maintaining the same depth. SiLU activations used instead of GELU.

This NFNet variant also uses ECA (Efficient Channel Attention) instead of SE (Squeeze-and-Excitation).

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 24.1
  • GMACs: 4.4
  • Activations (M): 10.5
  • Image size: train = 224 x 224, test = 288 x 288
• Papers:
  • High-Performance Large-Scale Image Recognition Without Normalization: https://arxiv.org/abs/2102.06171
  • Characterizing signal propagation to close the performance gap in unnormalized ResNets: https://arxiv.org/abs/2101.08692
• Original: https://github.com/huggingface/pytorch-image-models
• 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('eca_nfnet_l0.ra2_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

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Library