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	---
	license: mit
	library_name: timm
	tags:
	- image-classification
	- timm
	datasets:
	- imagenet-1k
	---
	# Model card for swin_small_patch4_window7_224.ms_in1k

	A Swin Transformer image classification model. Pretrained on ImageNet-1k by paper authors.


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 49.6
	- GMACs: 8.8
	- Activations (M): 27.5
	- Image size: 224 x 224
	- Papers:
	- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows: https://arxiv.org/abs/2103.14030
	- Original: https://github.com/microsoft/Swin-Transformer
	- 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('swin_small_patch4_window7_224.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(
	'swin_small_patch4_window7_224.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. for swin_base_patch4_window7_224 (NHWC output)
	# torch.Size([1, 56, 56, 128])
	# torch.Size([1, 28, 28, 256])
	# torch.Size([1, 14, 14, 512])
	# torch.Size([1, 7, 7, 1024])
	# e.g. for swinv2_cr_small_ns_224 (NCHW output)
	# torch.Size([1, 96, 56, 56])
	# torch.Size([1, 192, 28, 28])
	# torch.Size([1, 384, 14, 14])
	# torch.Size([1, 768, 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(
	'swin_small_patch4_window7_224.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 (ie.e a (batch_size, H, W, num_features) tensor for swin / swinv2
	# or (batch_size, num_features, H, W) for swinv2_cr

	output = model.forward_head(output, pre_logits=True)
	# output is (batch_size, num_features) 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
	@inproceedings{liu2021Swin,
	title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
	author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
	booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
	year={2021}
	}
	```
	```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}}
	}
	```