README.md

---
  license: apache-2.0
  tags:
  - mlx
  - mlx-image
  - vision
  - image-classification
  datasets:
  - imagenet-1k
  library_name: mlx-image
---
# vit_base_patch14_518.dinov2

A [Vision Transformer](https://arxiv.org/abs/2010.11929v2) image classification model trained on ImageNet-1k dataset with [DINOv2](https://arxiv.org/abs/2304.07193).

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="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=518)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)

model = create_model("vit_base_patch14_518.dinov2")
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_base_patch14_518.dinov2", num_classes=0)
model.eval()

embeds = model(x)

# second option
model = create_model("vit_base_patch14_518.dinov2")
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="attention_maps.png">
</div>