Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 1.4
- GMACs: 0.5
- Activations (M): 8.0
- Image size: 256 x 256
Papers:
- Separable Self-attention for Mobile Vision Transformers: https://arxiv.org/abs/2206.02680
Original: https://github.com/apple/ml-cvnets
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('mobilevitv2_050.cvnets_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(
    'mobilevitv2_050.cvnets_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, 64, 64, 64])
    #  torch.Size([1, 128, 32, 32])
    #  torch.Size([1, 192, 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(
    'mobilevitv2_050.cvnets_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

Explore the dataset and runtime metrics of this model in timm model results.

Citation

bibtex

@article{Mehta2022SeparableSF,
  title={Separable Self-attention for Mobile Vision Transformers},
  author={Sachin Mehta and Mohammad Rastegari},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.02680}
}

timm/mobilevitv2_050.cvnets_in1k

Model card for mobilevitv2_050.cvnets_in1k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Model Comparison

Citation

timm/
mobilevitv2_050.cvnets_in1k