Model card for regnety_032.ra_in1k

A RegNetY-3.2GF image classification model. Trained on ImageNet-1k by Ross Wightman in timm.

The timm RegNet implementation includes a number of enhancements not present in other implementations, including:

• stochastic depth
• gradient checkpointing
• layer-wise LR decay
• configurable output stride (dilation)
• configurable activation and norm layers
• option for a pre-activation bottleneck block used in RegNetV variant
• only known RegNetZ model definitions with pretrained weights

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 19.4
  • GMACs: 3.2
  • Activations (M): 11.3
  • Image size: train = 224 x 224, test = 288 x 288
• Papers:
  • Designing Network Design Spaces: https://arxiv.org/abs/2003.13678
• Dataset: ImageNet-1k
• 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('regnety_032.ra_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(
    'regnety_032.ra_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, 32, 112, 112])
    #  torch.Size([1, 72, 56, 56])
    #  torch.Size([1, 216, 28, 28])
    #  torch.Size([1, 576, 14, 14])
    #  torch.Size([1, 1512, 7, 7])

    print(o.shape)