levit_256.fb_dist_in1k – AI Model by timm | AlphaNeural AI

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timm/
levit_256.fb_dist_in1k

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image-classification

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Model card for levit_256.fb_dist_in1k

A LeViT image classification model using convolutional mode (using nn.Conv2d and nn.BatchNorm2d). Pretrained on ImageNet-1k using distillation by paper authors.

Model Details

Model Type: Image classification / feature backbone
Model Stats:
Papers:
- LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference: https://arxiv.org/abs/2104.01136
Original: https://github.com/facebookresearch/LeViT
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('levit_256.fb_dist_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)

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(
    'levit_256.fb_dist_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 (ie.e a (batch_size, num_features, H, W) tensor

output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor

Model Comparison

model	top1	top5	param_count	img_size
levit_384.fb_dist_in1k	82.596	96.012	39.13	224
levit_conv_384.fb_dist_in1k	82.596	96.012	39.13	224
levit_256.fb_dist_in1k	81.512	95.48	18.89	224
levit_conv_256.fb_dist_in1k	81.512	95.48	18.89	224
levit_conv_192.fb_dist_in1k	79.86	94.792	10.95	224
levit_192.fb_dist_in1k	79.858	94.792	10.95	224
levit_128.fb_dist_in1k	78.474	94.014	9.21	224
levit_conv_128.fb_dist_in1k	78.474	94.02	9.21	224
levit_128s.fb_dist_in1k	76.534	92.864	7.78	224
levit_conv_128s.fb_dist_in1k	76.532	92.864	7.78	224

Citation

bibtex

@InProceedings{Graham_2021_ICCV,
  author    = {Graham, Benjamin and El-Nouby, Alaaeldin and Touvron, Hugo and Stock, Pierre and Joulin, Armand and Jegou, Herve and Douze, Matthijs},
  title     = {LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2021},
  pages     = {12259-12269}
}

bibtex

@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/rwightman/pytorch-image-models}}
}