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	---
	license: mit
	library_name: timm
	tags:
	- image-classification
	- timm
	datasets:
	- imagenet-1k
	---
	# Model card for repvgg_b3.rvgg_in1k

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

	This model architecture is implemented using `timm`'s flexible [BYOBNet (Bring-Your-Own-Blocks Network)](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/byobnet.py).

	BYOBNet allows configuration of:
	* block / stage layout
	* stem layout
	* output stride (dilation)
	* activation and norm layers
	* channel and spatial / self-attention layers

	...and also includes `timm` features common to many other architectures, including:
	* stochastic depth
	* gradient checkpointing
	* layer-wise LR decay
	* per-stage feature extraction


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 123.1
	- GMACs: 29.2
	- Activations (M): 15.1
	- Image size: 224 x 224
	- Papers:
	- RepVGG: Making VGG-style ConvNets Great Again: https://arxiv.org/abs/2101.03697
	- Dataset: ImageNet-1k
	- Original: https://github.com/DingXiaoH/RepVGG

	## 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('repvgg_b3.rvgg_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(
	'repvgg_b3.rvgg_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, 192, 56, 56])
	# torch.Size([1, 384, 28, 28])
	# torch.Size([1, 768, 14, 14])
	# torch.Size([1, 2560, 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(
	'repvgg_b3.rvgg_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, 2560, 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
	@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}}
	}
	```
	```bibtex
	@inproceedings{ding2021repvgg,
	title={Repvgg: Making vgg-style convnets great again},
	author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
	booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages={13733--13742},
	year={2021}
	}
	```