---
tags:
- image-classification
- jax
- jaxnn
- vision-transformer
pipeline_tag: image-classification
library_name: jaxnn
license: apache-2.0
---

JaxNN conversion of the timm `vit_base_patch16_224.dino` Vision Transformer checkpoint.

## Model Details
- **Architecture:** vit_base_patch16_224
- **Source:** timm/vit_base_patch16_224.dino

# Model card for vit_base_patch16_224.dino

A Vision Transformer (ViT) image feature model. Trained with Self-Supervised DINO method.


## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 85.8
  - GMACs: 16.9
  - Activations (M): 16.5
  - Image size: 224 x 224
- **Papers:**
  - Emerging Properties in Self-Supervised Vision Transformers: https://arxiv.org/abs/2104.14294
  - 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/dino

## 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_base_patch16_224.dino", 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_base_patch16_224.dino",
    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
@inproceedings{caron2021emerging,
  title={Emerging properties in self-supervised vision transformers},
  author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J{'e}gou, Herv{'e} and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={9650--9660},
  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}}
}
```