timm
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Image Classification
timm
PyTorch
Safetensors

Model card for mobilenetv4_conv_small.e2400_r224_in1k

A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman.

Trained with timm scripts using hyper-parameters inspired by the MobileNet-V4 paper with timm enhancements.

NOTE: So far, these are the only known MNV4 weights. Official weights for Tensorflow models are unreleased.

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_small.e2400_r224_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_small.e2400_r224_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, 112, 112])
    #  torch.Size([1, 32, 56, 56])
    #  torch.Size([1, 64, 28, 28])
    #  torch.Size([1, 96, 14, 14])
    #  torch.Size([1, 960, 7, 7])

    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_small.e2400_r224_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, 7, 7) 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

@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}
}
@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}}
}
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