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	---
	tags:
	- image-classification
	- timm
	library_name: timm
	license: mit
	datasets:
	- imagenet-1k
	---
	# Model card for hrnet_w32.ms_in1k

	A HRNet image classification model. Trained on ImageNet-1k by paper authors.

	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 41.2
	- GMACs: 9.0
	- Activations (M): 22.0
	- Image size: 224 x 224
	- Papers:
	- Deep High-Resolution Representation Learning for Visual Recognition: https://arxiv.org/abs/1908.07919
	- Original: https://github.com/HRNet/HRNet-Image-Classification
	- 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_w32.ms_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_w32.ms_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_w32.ms_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](https://github.com/huggingface/pytorch-image-models/tree/main/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}
	}
	```