metadata
{}
---
license: apache-2.0
tags:
- mlx
- mlx-image
- vision
- image-classification
datasets:
- imagenet-1k
library_name: mlx-image
---
# vit_small_patch16_224.dino
A [Vision Transformer](https://arxiv.org/abs/2010.11929v2) image classification model trained on ImageNet-1k dataset with [DINO](https://arxiv.org/abs/2104.14294).
The model was trained in self-supervised fashion on ImageNet-1k dataset. No classification head was trained, only the backbone.
Disclaimer: This is a porting of the torch model weights to Apple MLX Framework.
<div align="center">
<img width="100%" alt="DINO illustration" src="/mlx-vision/vit_small_patch16_224.dino-mlxim/resolve/main/dino.gif">
</div>
## How to use
```bash
pip install mlx-image
```
Here is how to use this model for image classification:
```python
from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform
transform = ImageNetTransform(train=False, img_size=224)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)
model = create_model("vit_small_patch16_224.dino")
model.eval()
logits, attn_masks = model(x, attn_masks=True)
```
You can also use the embeds from layer before head:
```python
from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform
transform = ImageNetTransform(train=False, img_size=512)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)
# first option
model = create_model("vit_small_patch16_224.dino", num_classes=0)
model.eval()
embeds = model(x)
# second option
model = create_model("vit_small_patch16_224.dino")
model.eval()
embeds, attn_masks = model.get_features(x)
```
## Attention maps
You can visualize the attention maps using the `attn_masks` returned by the model. Go check the mlx-image [notebook](https://github.com/riccardomusmeci/mlx-image/blob/main/notebooks/dino_attention.ipynb).
<div align="center">
<img width="100%" alt="Attention Map" src="/mlx-vision/vit_small_patch16_224.dino-mlxim/resolve/main/attention_maps.png">
</div>