wide_resnet50_2.racm_in1k EU AI Act Compliance Profile

Model card for wide_resnet50_2.racm_in1k

A Wide-ResNet-B image classification model.

This model features:

• ReLU activations
• single layer 7x7 convolution with pooling
• 1x1 convolution shortcut downsample

Trained on ImageNet-1k in timm using recipe template described below.

Recipe details:

• RandAugment RACM recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as B recipe in ResNet Strikes Back.
• RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
• Step (exponential decay w/ staircase) LR schedule with warmup

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 68.9
  • GMACs: 11.4
  • Activations (M): 14.4
  • Image size: train = 224 x 224, test = 288 x 288
• Papers:
  • ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
  • Wide Residual Networks: https://arxiv.org/abs/1605.07146
  • Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
• Original: https://github.com/huggingface/pytorch-image-models

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('wide_resnet50_2.racm_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(

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