Model card for mobilenetv4_hybrid_medium.e500_r224_in1k

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

Trained with timm scripts using hyper-parameters (mostly) similar to those in the paper.

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

Model Details

Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 11.1
- GMACs: 1.0
- Activations (M): 6.4
- Image size: train = 224 x 224, test = 256 x 256
Dataset: ImageNet-1k
Papers:
- MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
Original: https://github.com/tensorflow/models/tree/master/official/vision

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_hybrid_medium.e500_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_hybrid_medium.e500_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, 48, 56, 56])
    #  torch.Size([1, 80, 28, 28])
    #  torch.Size([1, 160, 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_hybrid_medium.e500_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	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.e600_r384_in1k	84.266	15.734	96.936	3.064	37.76	448
mobilenetv4_hybrid_large.e600_r384_in1k	83.800	16.200	96.770	3.230	37.76	384
mobilenetv4_conv_large.e600_r384_in1k	83.392	16.608	96.622	3.378	32.59	448
mobilenetv4_conv_large.e600_r384_in1k	82.952	17.048	96.266	3.734	32.59	384
mobilenetv4_conv_large.e500_r256_in1k	82.674	17.326	96.31	3.69	32.59	320
mobilenetv4_conv_large.e500_r256_in1k	81.862	18.138	95.69	4.31	32.59	256
mobilenetv4_hybrid_medium.e500_r224_in1k	81.276	18.724	95.742	4.258	11.07	256
mobilenetv4_conv_medium.e500_r256_in1k	80.858	19.142	95.768	4.232	9.72	320
mobilenetv4_hybrid_medium.e500_r224_in1k	80.442	19.558	95.38	4.62	11.07	224
mobilenetv4_conv_blur_medium.e500_r224_in1k	80.142	19.858	95.298	4.702	9.72	256
mobilenetv4_conv_medium.e500_r256_in1k	79.928	20.072	95.184	4.816	9.72	256
mobilenetv4_conv_medium.e500_r224_in1k	79.808	20.192	95.186	4.814	9.72	256
mobilenetv4_conv_blur_medium.e500_r224_in1k	79.438	20.562	94.932	5.068	9.72	224
mobilenetv4_conv_medium.e500_r224_in1k	79.094	20.906	94.77	5.23	9.72	224
mobilenetv4_conv_small.e2400_r224_in1k	74.616	25.384	92.072	7.928	3.77	256
mobilenetv4_conv_small.e1200_r224_in1k	74.292	25.708	92.116	7.884	3.77	256
mobilenetv4_conv_small.e2400_r224_in1k	73.756	26.244	91.422	8.578	3.77	224
mobilenetv4_conv_small.e1200_r224_in1k	73.454	26.546	91.34	8.66	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}}
}

timm
/

mobilenetv4_hybrid_medium.e500_r224_in1k