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	---
	tags:
	- image-classification
	- timm
	library_name: timm
	license: other
	datasets:
	- imagenet-1k
	---
	# Model card for mobilevit_s.cvnets_in1k

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

	See license details at https://github.com/apple/ml-cvnets/blob/main/LICENSE

	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 5.6
	- GMACs: 2.0
	- Activations (M): 19.9
	- Image size: 256 x 256
	- Papers:
	- MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer: https://arxiv.org/abs/2110.02178
	- Original: https://github.com/apple/ml-cvnets
	- 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('mobilevit_s.cvnets_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(
	'mobilevit_s.cvnets_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, 32, 128, 128])
	# torch.Size([1, 64, 64, 64])
	# torch.Size([1, 96, 32, 32])
	# torch.Size([1, 128, 16, 16])
	# torch.Size([1, 640, 8, 8])

	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(
	'mobilevit_s.cvnets_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, 640, 8, 8) 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
	@inproceedings{mehta2022mobilevit,
	title={MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer},
	author={Sachin Mehta and Mohammad Rastegari},
	booktitle={International Conference on Learning Representations},
	year={2022}
	}
	```