mobilenetv4 conv small.e1200 r224 in1k EU AI Act Compliance Profile

Model card for mobilenetv4_conv_small.e1200_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 Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 3.8
  • GMACs: 0.2
  • Activations (M): 2.0
  • Image size: train = 224 x 224, test = 256 x 256
• Dataset: ImageNet-1k
• Original: https://github.com/tensorflow/models/tree/master/official/vision
• Papers:
  • MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518
  • PyTorch Image Models: https://github.com/huggingface/pytorch-image-models

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.e1200_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.e1200_r224_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model sp

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