metadata
tags:
- image-classification
- jax
- jaxnn
- vision-transformer
pipeline_tag: image-classification
library_name: jaxnn
license: cc-by-nc-4.0
JaxNN conversion of the timm vit_base_patch16_224.mae Vision Transformer checkpoint.
Model Details
- Architecture: vit_base_patch16_224
- Source: timm/vit_base_patch16_224.mae
Model card for vit_base_patch16_224.mae
A Vision Transformer (ViT) image feature model. Pretrained on ImageNet-1k with Self-Supervised Masked Autoencoder (MAE) method.
Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
- Params (M): 85.8
- GMACs: 17.6
- Activations (M): 23.9
- 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
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_base_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
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_base_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
@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},
}
@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}
}
@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}}
}