resnetv2_101x1_bit.goog_in21k / README.md

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	---
	license: apache-2.0
	library_name: timm
	tags:
	- image-classification
	- timm
	datasets:
	- imagenet-21k
	---
	# Model card for resnetv2_101x1_bit.goog_in21k

	A ResNet-V2-BiT (Big Transfer w/ pre-activation ResNet) image classification model. Trained on ImageNet-21k by paper authors.

	This model uses:
	* Group Normalization (GN) in combination with Weight Standardization (WS) instead of Batch Normalization (BN)..


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 87.2
	- GMACs: 8.1
	- Activations (M): 16.3
	- Image size: 224 x 224
	- Papers:
	- Big Transfer (BiT): General Visual Representation Learning: https://arxiv.org/abs/1912.11370
	- Identity Mappings in Deep Residual Networks: https://arxiv.org/abs/1603.05027
	- Dataset: ImageNet-21k
	- Original: https://github.com/google-research/big_transfer

	## 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('resnetv2_101x1_bit.goog_in21k', 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(
	'resnetv2_101x1_bit.goog_in21k',
	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, 256, 56, 56])
	# torch.Size([1, 512, 28, 28])
	# torch.Size([1, 1024, 14, 14])
	# torch.Size([1, 2048, 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(
	'resnetv2_101x1_bit.goog_in21k',
	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
	@inproceedings{Kolesnikov2019BigT,
	title={Big Transfer (BiT): General Visual Representation Learning},
	author={Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Joan Puigcerver and Jessica Yung and Sylvain Gelly and Neil Houlsby},
	booktitle={European Conference on Computer Vision},
	year={2019}
	}
	```
	```bibtex
	@article{He2016,
	author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
	title = {Identity Mappings in Deep Residual Networks},
	journal = {arXiv preprint arXiv:1603.05027},
	year = {2016}
	}
	```
	```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}}
	}
	```