Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 303.3
- GMACs: 61.6
- Activations (M): 63.5
- Image size: 224 x 224
Papers:
- Masked Autoencoders Are Scalable Vision Learners: https://arxiv.org/abs/2111.06377
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
Pretrain Dataset: ImageNet-1k
Original: https://github.com/facebookresearch/mae

Model Usage

Image Classification

python

from urllib.request import urlopen

import jax
from PIL import Image

import jaxnn

img = Image.open(urlopen(
    "https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg"
))

model = jaxnn.create_model("vit_large_patch16_224.mae", pretrained=True)
model.eval()

data_config = jaxnn.data.resolve_model_data_config(model)
transforms = jaxnn.data.create_transform(**data_config, is_training=False)

x = jax.numpy.expand_dims(transforms(img), 0)
output = model(x, deterministic=True)

top5_probabilities, top5_class_indices = jax.lax.top_k(
    jax.nn.softmax(output, axis=-1) * 100,
    k=5,
)

Image Embeddings

python

from urllib.request import urlopen

import jax
from PIL import Image

import jaxnn

img = Image.open(urlopen(
    "https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg"
))

model = jaxnn.create_model(
    "vit_large_patch16_224.mae",
    pretrained=True,
    num_classes=0,
)
model.eval()

data_config = jaxnn.data.resolve_model_data_config(model)
transforms = jaxnn.data.create_transform(**data_config, is_training=False)

x = jax.numpy.expand_dims(transforms(img), 0)
output = model(x, deterministic=True)

Citation

bibtex

@Article{MaskedAutoencoders2021,
  author  = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll{'a}r and Ross Girshick},
  journal = {arXiv:2111.06377},
  title   = {Masked Autoencoders Are Scalable Vision Learners},
  year    = {2021},
}

bibtex

@article{dosovitskiy2020vit,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  journal={ICLR},
  year={2021}
}

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/huggingface/pytorch-image-models}}
}

JaxNN/vit_large_patch16_224.mae

Model Details

Model card for vit_large_patch16_224.mae

Model Details

Model Usage

Image Classification

Image Embeddings

Citation

JaxNN/
vit_large_patch16_224.mae