diff --git a/README.md b/README.md
index bdc88e241..6328e1fda 100644
--- a/README.md
+++ b/README.md
@@ -72,19 +72,6 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
     masks, _, _ = predictor.predict(<input_prompts>)
 ```
 
-or from Hugging Face, as follows:
-
-```python
-import torch
-from sam2.sam2_image_predictor import SAM2ImagePredictor
-
-predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
-
-with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
-    predictor.set_image(<your_image>)
-    masks, _, _ = predictor.predict(<input_prompts>)
-```
-
 Please refer to the examples in [image_predictor_example.ipynb](./notebooks/image_predictor_example.ipynb) for static image use cases.
 
 SAM 2 also supports automatic mask generation on images just like SAM. Please see [automatic_mask_generator_example.ipynb](./notebooks/automatic_mask_generator_example.ipynb) for automatic mask generation in images.
@@ -110,7 +97,26 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
         ...
 ```
 
-or from Hugging Face, as follows:
+Please refer to the examples in [video_predictor_example.ipynb](./notebooks/video_predictor_example.ipynb) for details on how to add prompts, make refinements, and track multiple objects in videos.
+
+## Load from Hugging Face
+
+Alternatively, models can also be loaded from Hugging Face using the `from_pretrained` method:
+
+For image prediction:
+
+```python
+import torch
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+
+predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
+
+with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
+    predictor.set_image(<your_image>)
+    masks, _, _ = predictor.predict(<input_prompts>)
+```
+
+For video prediction:
 
 ```python
 import torch
@@ -123,8 +129,6 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
     masks, _, _ = predictor.predict(<input_prompts>)
 ```
 
-Please refer to the examples in [video_predictor_example.ipynb](./notebooks/video_predictor_example.ipynb) for details on how to add prompts, make refinements, and track multiple objects in videos.
-
 ## Model Description
 
 |      **Model**       | **Size (M)** |    **Speed (FPS)**     | **SA-V test (J&F)** | **MOSE val (J&F)** | **LVOS v2 (J&F)** |