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timm/
maxvit_rmlp_pico_rw_256.sw_in1k

Name: maxvit_rmlp_pico_rw_256.sw_in1k
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Model card for maxvit_rmlp_pico_rw_256.sw_in1k

A timm specific MaxViT (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in timm on ImageNet-1k by Ross Wightman.

ImageNet-1k training done on TPUs thanks to support of the TRC program.

Model Variants in 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): 7.5
- GMACs: 1.8
- Activations (M): 24.9
- Image size: 256 x 256
Papers:
- MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697
- Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
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_rmlp_pico_rw_256.sw_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_rmlp_pico_rw_256.sw_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, 32, 64, 64])
    #  torch.Size([1, 64, 32, 32])
    #  torch.Size([1, 128, 16, 16])
    #  torch.Size([1, 256, 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(
    'maxvit_rmlp_pico_rw_256.sw_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, 256, 8, 8) 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	88.53	98.64	21.76	475.77	534.14	1413.22
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	88.20	98.53	50.87	119.88	138.02	703.99
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	87.98	98.56	71.75	212.03	132.55	445.84
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	87.81	98.37	106.55	116.14	70.97	318.95
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	87.39	98.31	160.80	73.88	47.69	209.43
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	86.64	98.02	501.03	116.09	24.20	62.77
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	86.57	97.89	631.88	73.87	15.09	49.22
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	86.49	97.90	620.58	73.88	15.18	54.78
maxvit_base_tf_384.in1k	86.29	97.80	101.09	119.65	73.80	332.90
maxvit_large_tf_384.in1k	86.23	97.69	70.56	212.03	132.55	445.84
maxvit_small_tf_512.in1k	86.10	97.76	88.63	69.13	67.26	383.77
maxvit_tiny_tf_512.in1k	85.67	97.58	144.25	31.05	33.49	257.59
maxvit_small_tf_384.in1k	85.54	97.46	188.35	69.02	35.87	183.65
maxvit_tiny_tf_384.in1k	85.11	97.38	293.46	30.98	17.53	123.42
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	84.90	96.96	1025.45	41.72	8.11	40.13
maxvit_base_tf_224.in1k	84.85	96.99	358.25	119.47	24.04	95.01
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	84.61	96.74	625.81	73.88	15.18	54.78
maxvit_rmlp_small_rw_224.sw_in1k	84.49	96.76	693.82	64.90	10.75	49.30
maxvit_small_tf_224.in1k	84.43	96.83	647.96	68.93	11.66	53.17
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	83.62	96.38	989.59	41.72	8.04	34.60
maxvit_tiny_rw_224.sw_in1k	83.50	96.50	1100.53	29.06	5.11	33.11
maxvit_tiny_tf_224.in1k	83.41	96.59	1004.94	30.92	5.60	35.78
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	83.11	96.33	1276.88	23.70	6.26	23.05
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	82.96	96.26	1283.24	15.50	4.47	31.92
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	82.39	96.19	1600.14	27.44	4.67	22.04
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	82.05	95.87	2109.09	15.15	2.62	20.34
coatnext_nano_rw_224.sw_in1k	81.95	95.92	2525.52	14.70	2.47	12.80
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	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	81.95	95.92	2525.52	14.70	2.47	12.80
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	82.05	95.87	2109.09	15.15	2.62	20.34
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	82.39	96.19	1600.14	27.44	4.67	22.04
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	83.03	96.34	1341.24	16.78	4.37	26.05
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	83.11	96.33	1276.88	23.70	6.26	23.05
maxvit_nano_rw_256.sw_in1k	82.93	96.23	1218.17	15.45	4.46	30.28
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	83.36	96.45	1093.03	41.69	7.85	35.47
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	83.41	96.59	1004.94	30.92	5.60	35.78
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	84.23	96.78	807.21	29.15	6.77	46.92
maxvit_rmlp_small_rw_224.sw_in1k	84.49	96.76	693.82	64.90	10.75	49.30
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	86.57	97.89	631.88	73.87	15.09	49.22
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	86.49	97.90	620.58	73.88	15.18	54.78
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	86.64	98.02	501.03	116.09	24.20	62.77
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	84.85	96.99	358.25	119.47	24.04	95.01
maxvit_tiny_tf_384.in1k	85.11	97.38	293.46	30.98	17.53	123.42
maxvit_large_tf_224.in1k	84.93	96.97	247.71	211.79	43.68	127.35
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	87.39	98.31	160.80	73.88	47.69	209.43
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	85.67	97.58	144.25	31.05	33.49	257.59
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	87.92	98.54	104.71	119.65	73.80	332.90
maxvit_base_tf_384.in1k	86.29	97.80	101.09	119.65	73.80	332.90
maxvit_small_tf_512.in1k	86.10	97.76	88.63	69.13	67.26	383.77
maxvit_large_tf_384.in21k_ft_in1k	87.98	98.56	71.75	212.03	132.55	445.84
maxvit_large_tf_384.in1k	86.23	97.69	70.56	212.03	132.55	445.84
maxvit_base_tf_512.in21k_ft_in1k	88.20	98.53	50.87	119.88	138.02	703.99
maxvit_base_tf_512.in1k	86.60	97.92	50.75	119.88	138.02	703.99
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	88.04	98.40	36.42	212.33	244.75	942.15
maxvit_large_tf_512.in1k	86.52	97.88	36.04	212.33	244.75	942.15
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}
}

timm/maxvit_rmlp_pico_rw_256.sw_in1k

Model card for maxvit_rmlp_pico_rw_256.sw_in1k

Model Variants in maxxvit.py

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Model Comparison

By Top-1

By Throughput (samples / sec)

Citation

timm/
maxvit_rmlp_pico_rw_256.sw_in1k