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README.md

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+---
+license: apache-2.0
+library_name: mlx-image
+tags:
+- mlx
+- mlx-image
+- vision
+- image-classification
+datasets:
+- imagenet-1k
+---
+# vit_small_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_small_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_small_patch14_518.dinov2", num_classes=0)
+model.eval()
+
+embeds = model(x)
+
+# second option
+model = create_model("vit_small_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/notebooks/dino_attention.ipynb).
+
+<div align="center">
+  <img width="100%" alt="Attention Map" src="attention_maps.png">
+</div>
+