convnext_small.in12k_ft_in1k EU AI Act Compliance Profile

Model card for convnext_small.in12k_ft_in1k

A ConvNeXt image classification model. Pretrained in timm on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman.

ImageNet-12k training done on TPUs thanks to support of the TRC program.

Fine-tuning performed on 8x GPU Lambda Labs cloud instances.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 50.2
  • GMACs: 8.7
  • Activations (M): 21.6
  • Image size: train = 224 x 224, test = 288 x 288
• Papers:
  • A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
• Original: https://github.com/huggingface/pytorch-image-models
• Dataset: ImageNet-1k
• Pretrain Dataset: ImageNet-12k

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('convnext_small.in12k_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(
    'convnext_small.in12k_ft_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data

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