Update README.md

README.md

@@ -1,81 +1,79 @@
----
-{}
+
 ---
 
-    ---
-    license: apache-2.0
-    tags:
-    - mlx
-    - mlx-image
-    - vision
-    - image-classification
-    datasets:
-    - imagenet-1k
-    library_name: mlx-image
-    ---
-    # vit_base_patch16_224.dino
+---
+license: apache-2.0
+tags:
+- mlx
+- mlx-image
+- vision
+- image-classification
+datasets:
+- imagenet-1k
+library_name: mlx-image
+---
+# vit_base_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).
+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.
+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.
+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>
+<div align="center">
+<img width="100%" alt="DINO illustration" src="dino.gif">
+</div>
 
 
-    ## How to use
-    ```bash
-    pip install mlx-image
-    ```
+## How to use
+```bash
+pip install mlx-image
+```
 
-    Here is how to use this model for image classification:
+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
+```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)
+transform = ImageNetTransform(train=False, img_size=224)
+x = transform(read_rgb("cat.png"))
+x = mx.expand_dims(x, 0)
 
-    model = create_model("vit_base_patch16_224.dino")
-    model.eval()
+model = create_model("vit_base_patch16_224.dino")
+model.eval()
 
-    logits, attn_masks = model(x, attn_masks=True)
-    ```
+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
+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)
+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_patch16_224.dino", num_classes=0)
-    model.eval()
+# first option
+model = create_model("vit_base_patch16_224.dino", num_classes=0)
+model.eval()
 
-    embeds = model(x)
+embeds = model(x)
 
-    # second option
-    model = create_model("vit_base_patch16_224.dino")
-    model.eval()
+# second option
+model = create_model("vit_base_patch16_224.dino")
+model.eval()
 
-    embeds, attn_masks = model.get_features(x)
-    ```
+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).
+## 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>
+<div align="center">
+<img width="100%" alt="Attention Map" src="attention_maps.png">
+</div>
 
-