Update README.md

README.md

@@ -1,67 +1,64 @@
----
-{}
----
-
-    ---
-    license: apache-2.0
-    tags:
-    - mlx
-    - mlx-image
-    - vision
-    - image-classification
-    datasets:
-    - imagenet-1k
-    library_name: mlx-image
-    ---
-    # regnet_y_800mf
-
-    A RegNetY-800MF image classification model. Pretrained in ImageNet by torchvision contributors (see ImageNet1K-V2 weight details https://github.com/pytorch/vision/issues/3995#new-recipe).
-
-    Disclaimer: This is a porting of the torch model weights to Apple MLX Framework.
-
-    ## 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("regnet_y_800mf")
-    model.eval()
-
-    logits = model(x)
-    ```
-
-    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=224)
-    x = transform(read_rgb("cat.png"))
-    x = mx.expand_dims(x, 0)
-
-    # first option
-    model = create_model("regnet_y_800mf", num_classes=0)
-    model.eval()
-
-    embeds = model(x)
-
-    # second option
-    model = create_model("regnet_y_800mf")
-    model.eval()
-
-    embeds = model.get_features(x)
-    ```
+---
+  license: apache-2.0
+  tags:
+  - mlx
+  - mlx-image
+  - vision
+  - image-classification
+  datasets:
+  - imagenet-1k
+  library_name: mlx-image
+  ---
+  # regnet_y_800mf
+
+  A RegNetY-800MF image classification model. Pretrained in ImageNet by torchvision contributors (see ImageNet1K-V2 weight details https://github.com/pytorch/vision/issues/3995#new-recipe).
+
+  Disclaimer: This is a porting of the torch model weights to Apple MLX Framework.
+
+  ## 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("regnet_y_800mf")
+  model.eval()
+
+  logits = model(x)
+  ```
+
+  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=224)
+  x = transform(read_rgb("cat.png"))
+  x = mx.expand_dims(x, 0)
+
+  # first option
+  model = create_model("regnet_y_800mf", num_classes=0)
+  model.eval()
+
+  embeds = model(x)
+
+  # second option
+  model = create_model("regnet_y_800mf")
+  model.eval()
+
+  embeds = model.get_features(x)
+  ```