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	---
	license: mit
	library_name: timm
	tags:
	- image-classification
	- timm
	datasets:
	- imagenet-1k
	---
	# Model card for swinv2_tiny_window8_256.ms_in1k

	A Swin Transformer V2 image classification model. Pretrained on ImageNet-1k by paper authors.


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 28.3
	- GMACs: 6.0
	- Activations (M): 24.6
	- Image size: 256 x 256
	- Papers:
	- Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
	- 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('swinv2_tiny_window8_256.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(
	'swinv2_tiny_window8_256.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(
	'swinv2_tiny_window8_256.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{liu2021swinv2,
	title={Swin Transformer V2: Scaling Up Capacity and Resolution},
	author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
	booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
	year={2022}
	}
	```
	```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}}
	}
	```