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timm/
hrnet_w18_small_v2.gluon_in1k

Name: hrnet_w18_small_v2.gluon_in1k
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Model card for hrnet_w18_small_v2.gluon_in1k

A HRNet image classification model. Trained on ImageNet-1k by Apache MXNet GluonCV authors.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 15.6
- GMACs: 2.6
- Activations (M): 9.6
- Image size: 224 x 224
Papers:
- Deep High-Resolution Representation Learning for Visual Recognition: https://arxiv.org/abs/1908.07919
Original: https://cv.gluon.ai
Dataset: ImageNet-1k

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('hrnet_w18_small_v2.gluon_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(
    'hrnet_w18_small_v2.gluon_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, 64, 112, 112])
    #  torch.Size([1, 128, 56, 56])
    #  torch.Size([1, 256, 28, 28])
    #  torch.Size([1, 512, 14, 14])
    #  torch.Size([1, 1024, 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(
    'hrnet_w18_small_v2.gluon_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, 2048, 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

@article{WangSCJDZLMTWLX19,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal   = {TPAMI}
  year={2019}
}

timm/hrnet_w18_small_v2.gluon_in1k

Model card for hrnet_w18_small_v2.gluon_in1k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Model Comparison

Citation

timm/
hrnet_w18_small_v2.gluon_in1k