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Image Classification
timm
PyTorch
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Model card for mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k

A MobileNet-V4 (w/ AvgPool Anti-Aliasing) image classification model. Pretrained on ImageNet-12k and fine-tuned on ImageNet-1k by Ross Wightman.

Model Details

Model Usage

Image Classification

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('mobilenetv4_conv_aa_large.e230_r384_in12k_ft_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

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(
    'mobilenetv4_conv_aa_large.e230_r384_in12k_ft_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, 24, 192, 192])
    #  torch.Size([1, 48, 96, 96])
    #  torch.Size([1, 96, 48, 48])
    #  torch.Size([1, 192, 24, 24])
    #  torch.Size([1, 960, 12, 12])

    print(o.shape)

Image Embeddings

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(
    'mobilenetv4_conv_aa_large.e230_r384_in12k_ft_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, 960, 12, 12) 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 param_count img_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k 84.99 97.294 32.59 544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k 84.772 97.344 32.59 480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k 84.64 97.114 32.59 448
mobilenetv4_hybrid_large.ix_e600_r384_in1k 84.356 96.892 37.76 448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k 84.314 97.102 32.59 384
mobilenetv4_hybrid_large.e600_r384_in1k 84.266 96.936 37.76 448
mobilenetv4_hybrid_large.ix_e600_r384_in1k 83.990 96.702 37.76 384
mobilenetv4_conv_aa_large.e600_r384_in1k 83.824 96.734 32.59 480
mobilenetv4_hybrid_large.e600_r384_in1k 83.800 96.770 37.76 384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k 83.394 96.760 11.07 448
mobilenetv4_conv_large.e600_r384_in1k 83.392 96.622 32.59 448
mobilenetv4_conv_aa_large.e600_r384_in1k 83.244 96.392 32.59 384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k 82.99 96.67 11.07 320
mobilenetv4_hybrid_medium.ix_e550_r384_in1k 82.968 96.474 11.07 384
mobilenetv4_conv_large.e600_r384_in1k 82.952 96.266 32.59 384
mobilenetv4_conv_large.e500_r256_in1k 82.674 96.31 32.59 320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k 82.492 96.278 11.07 320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k 82.364 96.256 11.07 256
mobilenetv4_conv_large.e500_r256_in1k 81.862 95.69 32.59 256
resnet50d.ra4_e3600_r224_in1k 81.838 95.922 25.58 288
mobilenetv3_large_150d.ra4_e3600_r256_in1k 81.806 95.9 14.62 320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k 81.446 95.704 11.07 256
efficientnet_b1.ra4_e3600_r240_in1k 81.440 95.700 7.79 288
mobilenetv4_hybrid_medium.e500_r224_in1k 81.276 95.742 11.07 256
resnet50d.ra4_e3600_r224_in1k 80.952 95.384 25.58 224
mobilenetv3_large_150d.ra4_e3600_r256_in1k 80.944 95.448 14.62 256
mobilenetv4_conv_medium.e500_r256_in1k 80.858 95.768 9.72 320
mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k 80.680 95.442 8.46 256
mobilenetv4_hybrid_medium.e500_r224_in1k 80.442 95.38 11.07 224
efficientnet_b1.ra4_e3600_r240_in1k 80.406 95.152 7.79 240
mobilenetv4_conv_blur_medium.e500_r224_in1k 80.142 95.298 9.72 256
mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k 80.130 95.002 8.46 224
mobilenetv4_conv_medium.e500_r256_in1k 79.928 95.184 9.72 256
mobilenetv4_conv_medium.e500_r224_in1k 79.808 95.186 9.72 256
mobilenetv4_conv_blur_medium.e500_r224_in1k 79.438 94.932 9.72 224
efficientnet_b0.ra4_e3600_r224_in1k 79.364 94.754 5.29 256
mobilenetv4_conv_medium.e500_r224_in1k 79.094 94.77 9.72 224
efficientnet_b0.ra4_e3600_r224_in1k 78.584 94.338 5.29 224
mobilenetv1_125.ra4_e3600_r224_in1k 77.600 93.804 6.27 256
mobilenetv3_large_100.ra4_e3600_r224_in1k 77.164 93.336 5.48 256
mobilenetv1_125.ra4_e3600_r224_in1k 76.924 93.234 6.27 224
mobilenetv1_100h.ra4_e3600_r224_in1k 76.596 93.272 5.28 256
mobilenetv3_large_100.ra4_e3600_r224_in1k 76.310 92.846 5.48 224
mobilenetv1_100.ra4_e3600_r224_in1k 76.094 93.004 4.23 256
mobilenetv1_100h.ra4_e3600_r224_in1k 75.662 92.504 5.28 224
mobilenetv1_100.ra4_e3600_r224_in1k 75.382 92.312 4.23 224
mobilenetv4_conv_small.e2400_r224_in1k 74.616 92.072 3.77 256
mobilenetv4_conv_small.e1200_r224_in1k 74.292 92.116 3.77 256
mobilenetv4_conv_small.e2400_r224_in1k 73.756 91.422 3.77 224
mobilenetv4_conv_small.e1200_r224_in1k 73.454 91.34 3.77 224

Citation

@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}}
}
@article{qin2024mobilenetv4,
  title={MobileNetV4-Universal Models for the Mobile Ecosystem},
  author={Qin, Danfeng and Leichner, Chas and Delakis, Manolis and Fornoni, Marco and Luo, Shixin and Yang, Fan and Wang, Weijun and Banbury, Colby and Ye, Chengxi and Akin, Berkin and others},
  journal={arXiv preprint arXiv:2404.10518},
  year={2024}
}
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