Update model config and README

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	---
	tags:
	- image-classification
	- timm
	library_name: timm
	license: apache-2.0
	datasets:
	- imagenet-1k
	---
	# Model card for maxvit_tiny_tf_512.in1k

	An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors.
	Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman.

	### Model Variants in [maxxvit.py](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/maxxvit.py)

	MaxxViT covers a number of related model architectures that share a common structure including:
	- CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages.
	- MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid).
	- CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm).
	- MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm).
	- MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate.

	Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string `rw` are `timm` specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations.
	All models with the string `tf` are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released.

	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 31.0
	- GMACs: 33.5
	- Activations (M): 257.6
	- Image size: 512 x 512
	- Papers:
	- MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697
	- 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('maxvit_tiny_tf_512.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(
	'maxvit_tiny_tf_512.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, 256, 256])
	# torch.Size([1, 64, 128, 128])
	# torch.Size([1, 128, 64, 64])
	# torch.Size([1, 256, 32, 32])
	# torch.Size([1, 512, 16, 16])

	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(
	'maxvit_tiny_tf_512.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, 512, 16, 16) shaped tensor

	output = model.forward_head(output, pre_logits=True)
	# output is a (1, num_features) shaped tensor
	```

	## Model Comparison
	### By Top-1
	\|model \|top1 \|top5 \|samples / sec \|Params (M) \|GMAC \|Act (M)\|
	\|------------------------------------------------------------------------------------------------------------------------\|----:\|----:\|--------------:\|--------------:\|-----:\|------:\|
	\|[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) \|88.53\|98.64\| 21.76\| 475.77\|534.14\|1413.22\|
	\|[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) \|88.32\|98.54\| 42.53\| 475.32\|292.78\| 668.76\|
	\|[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) \|88.20\|98.53\| 50.87\| 119.88\|138.02\| 703.99\|
	\|[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) \|88.04\|98.40\| 36.42\| 212.33\|244.75\| 942.15\|
	\|[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) \|87.98\|98.56\| 71.75\| 212.03\|132.55\| 445.84\|
	\|[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) \|87.92\|98.54\| 104.71\| 119.65\| 73.80\| 332.90\|
	\|[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) \|87.81\|98.37\| 106.55\| 116.14\| 70.97\| 318.95\|
	\|[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) \|87.47\|98.37\| 149.49\| 116.09\| 72.98\| 213.74\|
	\|[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) \|87.39\|98.31\| 160.80\| 73.88\| 47.69\| 209.43\|
	\|[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) \|86.89\|98.02\| 375.86\| 116.14\| 23.15\| 92.64\|
	\|[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) \|86.64\|98.02\| 501.03\| 116.09\| 24.20\| 62.77\|
	\|[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) \|86.60\|97.92\| 50.75\| 119.88\|138.02\| 703.99\|
	\|[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) \|86.57\|97.89\| 631.88\| 73.87\| 15.09\| 49.22\|
	\|[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) \|86.52\|97.88\| 36.04\| 212.33\|244.75\| 942.15\|
	\|[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) \|86.49\|97.90\| 620.58\| 73.88\| 15.18\| 54.78\|
	\|[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) \|86.29\|97.80\| 101.09\| 119.65\| 73.80\| 332.90\|
	\|[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) \|86.23\|97.69\| 70.56\| 212.03\|132.55\| 445.84\|
	\|[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) \|86.10\|97.76\| 88.63\| 69.13\| 67.26\| 383.77\|
	\|[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) \|85.67\|97.58\| 144.25\| 31.05\| 33.49\| 257.59\|
	\|[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) \|85.54\|97.46\| 188.35\| 69.02\| 35.87\| 183.65\|
	\|[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) \|85.11\|97.38\| 293.46\| 30.98\| 17.53\| 123.42\|
	\|[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) \|84.93\|96.97\| 247.71\| 211.79\| 43.68\| 127.35\|
	\|[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) \|84.90\|96.96\| 1025.45\| 41.72\| 8.11\| 40.13\|
	\|[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) \|84.85\|96.99\| 358.25\| 119.47\| 24.04\| 95.01\|
	\|[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) \|84.63\|97.06\| 575.53\| 66.01\| 14.67\| 58.38\|
	\|[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) \|84.61\|96.74\| 625.81\| 73.88\| 15.18\| 54.78\|
	\|[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) \|84.49\|96.76\| 693.82\| 64.90\| 10.75\| 49.30\|
	\|[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) \|84.43\|96.83\| 647.96\| 68.93\| 11.66\| 53.17\|
	\|[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) \|84.23\|96.78\| 807.21\| 29.15\| 6.77\| 46.92\|
	\|[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) \|83.62\|96.38\| 989.59\| 41.72\| 8.04\| 34.60\|
	\|[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) \|83.50\|96.50\| 1100.53\| 29.06\| 5.11\| 33.11\|
	\|[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) \|83.41\|96.59\| 1004.94\| 30.92\| 5.60\| 35.78\|
	\|[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) \|83.36\|96.45\| 1093.03\| 41.69\| 7.85\| 35.47\|
	\|[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) \|83.11\|96.33\| 1276.88\| 23.70\| 6.26\| 23.05\|
	\|[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) \|83.03\|96.34\| 1341.24\| 16.78\| 4.37\| 26.05\|
	\|[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) \|82.96\|96.26\| 1283.24\| 15.50\| 4.47\| 31.92\|
	\|[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) \|82.93\|96.23\| 1218.17\| 15.45\| 4.46\| 30.28\|
	\|[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) \|82.39\|96.19\| 1600.14\| 27.44\| 4.67\| 22.04\|
	\|[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) \|82.39\|95.84\| 1831.21\| 27.44\| 4.43\| 18.73\|
	\|[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) \|82.05\|95.87\| 2109.09\| 15.15\| 2.62\| 20.34\|
	\|[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) \|81.95\|95.92\| 2525.52\| 14.70\| 2.47\| 12.80\|
	\|[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) \|81.70\|95.64\| 2344.52\| 15.14\| 2.41\| 15.41\|
	\|[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) \|80.53\|95.21\| 1594.71\| 7.52\| 1.85\| 24.86\|


	### By Throughput (samples / sec)
	\|model \|top1 \|top5 \|samples / sec \|Params (M) \|GMAC \|Act (M)\|
	\|------------------------------------------------------------------------------------------------------------------------\|----:\|----:\|--------------:\|--------------:\|-----:\|------:\|
	\|[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) \|81.95\|95.92\| 2525.52\| 14.70\| 2.47\| 12.80\|
	\|[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) \|81.70\|95.64\| 2344.52\| 15.14\| 2.41\| 15.41\|
	\|[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) \|82.05\|95.87\| 2109.09\| 15.15\| 2.62\| 20.34\|
	\|[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) \|82.39\|95.84\| 1831.21\| 27.44\| 4.43\| 18.73\|
	\|[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) \|82.39\|96.19\| 1600.14\| 27.44\| 4.67\| 22.04\|
	\|[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) \|80.53\|95.21\| 1594.71\| 7.52\| 1.85\| 24.86\|
	\|[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) \|83.03\|96.34\| 1341.24\| 16.78\| 4.37\| 26.05\|
	\|[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) \|82.96\|96.26\| 1283.24\| 15.50\| 4.47\| 31.92\|
	\|[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) \|83.11\|96.33\| 1276.88\| 23.70\| 6.26\| 23.05\|
	\|[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) \|82.93\|96.23\| 1218.17\| 15.45\| 4.46\| 30.28\|
	\|[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) \|83.50\|96.50\| 1100.53\| 29.06\| 5.11\| 33.11\|
	\|[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) \|83.36\|96.45\| 1093.03\| 41.69\| 7.85\| 35.47\|
	\|[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) \|84.90\|96.96\| 1025.45\| 41.72\| 8.11\| 40.13\|
	\|[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) \|83.41\|96.59\| 1004.94\| 30.92\| 5.60\| 35.78\|
	\|[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) \|83.62\|96.38\| 989.59\| 41.72\| 8.04\| 34.60\|
	\|[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) \|84.23\|96.78\| 807.21\| 29.15\| 6.77\| 46.92\|
	\|[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) \|84.49\|96.76\| 693.82\| 64.90\| 10.75\| 49.30\|
	\|[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) \|84.43\|96.83\| 647.96\| 68.93\| 11.66\| 53.17\|
	\|[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) \|86.57\|97.89\| 631.88\| 73.87\| 15.09\| 49.22\|
	\|[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) \|84.61\|96.74\| 625.81\| 73.88\| 15.18\| 54.78\|
	\|[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) \|86.49\|97.90\| 620.58\| 73.88\| 15.18\| 54.78\|
	\|[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) \|84.63\|97.06\| 575.53\| 66.01\| 14.67\| 58.38\|
	\|[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) \|86.64\|98.02\| 501.03\| 116.09\| 24.20\| 62.77\|
	\|[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) \|86.89\|98.02\| 375.86\| 116.14\| 23.15\| 92.64\|
	\|[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) \|84.85\|96.99\| 358.25\| 119.47\| 24.04\| 95.01\|
	\|[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) \|85.11\|97.38\| 293.46\| 30.98\| 17.53\| 123.42\|
	\|[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) \|84.93\|96.97\| 247.71\| 211.79\| 43.68\| 127.35\|
	\|[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) \|85.54\|97.46\| 188.35\| 69.02\| 35.87\| 183.65\|
	\|[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) \|87.39\|98.31\| 160.80\| 73.88\| 47.69\| 209.43\|
	\|[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) \|87.47\|98.37\| 149.49\| 116.09\| 72.98\| 213.74\|
	\|[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) \|85.67\|97.58\| 144.25\| 31.05\| 33.49\| 257.59\|
	\|[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) \|87.81\|98.37\| 106.55\| 116.14\| 70.97\| 318.95\|
	\|[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) \|87.92\|98.54\| 104.71\| 119.65\| 73.80\| 332.90\|
	\|[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) \|86.29\|97.80\| 101.09\| 119.65\| 73.80\| 332.90\|
	\|[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) \|86.10\|97.76\| 88.63\| 69.13\| 67.26\| 383.77\|
	\|[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) \|87.98\|98.56\| 71.75\| 212.03\|132.55\| 445.84\|
	\|[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) \|86.23\|97.69\| 70.56\| 212.03\|132.55\| 445.84\|
	\|[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) \|88.20\|98.53\| 50.87\| 119.88\|138.02\| 703.99\|
	\|[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) \|86.60\|97.92\| 50.75\| 119.88\|138.02\| 703.99\|
	\|[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) \|88.32\|98.54\| 42.53\| 475.32\|292.78\| 668.76\|
	\|[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) \|88.04\|98.40\| 36.42\| 212.33\|244.75\| 942.15\|
	\|[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) \|86.52\|97.88\| 36.04\| 212.33\|244.75\| 942.15\|
	\|[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) \|88.53\|98.64\| 21.76\| 475.77\|534.14\|1413.22\|

	## 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
	@article{tu2022maxvit,
	title={MaxViT: Multi-Axis Vision Transformer},
	author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
	journal={ECCV},
	year={2022},
	}
	```
	```bibtex
	@article{dai2021coatnet,
	title={CoAtNet: Marrying Convolution and Attention for All Data Sizes},
	author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing},
	journal={arXiv preprint arXiv:2106.04803},
	year={2021}
	}
	```