Update README.md

README.md

@@ -1,64 +1,62 @@
-
 ---
-  license: apache-2.0
-  tags:
-  - mlx
-  - mlx-image
-  - vision
-  - image-classification
-  datasets:
-  - imagenet-1k
-  library_name: mlx-image
-  ---
-  # regnet_y_800mf
+license: apache-2.0
+datasets:
+- ILSVRC/imagenet-1k
+tags:
+- mlx
+- mlx-image
+- vision
+- image-classification
+library_name: mlx-image
+---
 
-  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).
+# regnet_y_800mf
 
-  Disclaimer: This is a porting of the torch model weights to Apple MLX Framework.
+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).
 
-  ## How to use
-  ```bash
-  pip install mlx-image
-  ```
+Disclaimer: This is a porting of the torch model weights to Apple MLX Framework.
 
-  Here is how to use this model for image classification:
+## How to use
+```bash
+pip install mlx-image
+```
 
-  ```python
-  from mlxim.model import create_model
-  from mlxim.io import read_rgb
-  from mlxim.transform import ImageNetTransform
+Here is how to use this model for image classification:
 
-  transform = ImageNetTransform(train=False, img_size=224)
-  x = transform(read_rgb("cat.png"))
-  x = mx.expand_dims(x, 0)
+```python
+from mlxim.model import create_model
+from mlxim.io import read_rgb
+from mlxim.transform import ImageNetTransform
 
-  model = create_model("regnet_y_800mf")
-  model.eval()
+transform = ImageNetTransform(train=False, img_size=224)
+x = transform(read_rgb("cat.png"))
+x = mx.expand_dims(x, 0)
 
-  logits = model(x)
-  ```
+model = create_model("regnet_y_800mf")
+model.eval()
 
-  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
+logits = model(x)
+```
 
-  transform = ImageNetTransform(train=False, img_size=224)
-  x = transform(read_rgb("cat.png"))
-  x = mx.expand_dims(x, 0)
+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
 
-  # first option
-  model = create_model("regnet_y_800mf", num_classes=0)
-  model.eval()
+transform = ImageNetTransform(train=False, img_size=224)
+x = transform(read_rgb("cat.png"))
+x = mx.expand_dims(x, 0)
 
-  embeds = model(x)
+# first option
+model = create_model("regnet_y_800mf", num_classes=0)
+model.eval()
 
-  # second option
-  model = create_model("regnet_y_800mf")
-  model.eval()
+embeds = model(x)
 
-  embeds = model.get_features(x)
-  ```
+# second option
+model = create_model("regnet_y_800mf")
+model.eval()
 
-    
+embeds = model.get_features(x)
+```