diff --git a/README.md b/README.md
index 21104c7..bdc88e2 100644
--- a/README.md
+++ b/README.md
@@ -58,6 +58,22 @@ Then SAM 2 can be used in a few lines as follows for image and video prediction.
 
 SAM 2 has all the capabilities of [SAM](https://github.com/facebookresearch/segment-anything) on static images, and we provide image prediction APIs that closely resemble SAM for image use cases. The `SAM2ImagePredictor` class has an easy interface for image prompting.
 
+```python
+import torch
+from sam2.build_sam import build_sam2
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+
+checkpoint = "./checkpoints/sam2_hiera_large.pt"
+model_cfg = "sam2_hiera_l.yaml"
+predictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))
+
+with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
+    predictor.set_image(<your_image>)
+    masks, _, _ = predictor.predict(<input_prompts>)
+```
+
+or from Hugging Face, as follows:
+
 ```python
 import torch
 from sam2.sam2_image_predictor import SAM2ImagePredictor
@@ -94,6 +110,19 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
         ...
 ```
 
+or from Hugging Face, as follows:
+
+```python
+import torch
+from sam2.sam2_video_predictor import SAM2VideoPredictor
+
+predictor = SAM2VideoPredictor.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 [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