Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 8.2
- GMACs: 0.7
- Activations (M): 2.7
- Image size: 224 x 224
Papers:
- Neural Architecture Design for GPU-Efficient Networks: https://arxiv.org/abs/2006.14090
Dataset: ImageNet-1k
Original: https://github.com/idstcv/GPU-Efficient-Networks

Model Usage

Image Classification

python

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('gernet_s.idstcv_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

python

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(
    'gernet_s.idstcv_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, 13, 112, 112])
    #  torch.Size([1, 48, 56, 56])
    #  torch.Size([1, 48, 28, 28])
    #  torch.Size([1, 384, 14, 14])
    #  torch.Size([1, 1920, 7, 7])

    print(o.shape)

Image Embeddings

python

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(
    'gernet_s.idstcv_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)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1920, 7, 7) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

Model Comparison

Explore the dataset and runtime metrics of this model in timm model results.

Citation

bibtex

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}

bibtex

@misc{lin2020neural,
  title={Neural Architecture Design for GPU-Efficient Networks},
  author={Ming Lin and Hesen Chen and Xiuyu Sun and Qi Qian and Hao Li and Rong Jin},
  year={2020},
  eprint={2006.14090},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

timm/gernet_s.idstcv_in1k

Model card for gernet_s.idstcv_in1k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Model Comparison

Citation

timm/
gernet_s.idstcv_in1k