Update README.md

README.md

@@ -45,7 +45,7 @@ The data curation pipeline leading to the clean videos in the Surg-3M dataset is
 
 Usage
 --------
-Video classification models are employed in the step 2 of the data curation pipeline to classify a video storyboard as either surgical or non-surgical, the models usage is as follows:
+**Video classification models** are employed in the step 2 of the data curation pipeline to classify a video storyboard as either surgical or non-surgical, the models usage is as follows:
    ```python
    import torch
    from PIL import Image
@@ -72,7 +72,7 @@ Video classification models are employed in the step 2 of the data curation pipe
    outputs = net(img_tensor)
    ```
 
-Frame classification models are used in the step 3 of the data curation pipeline to classify a frame as either surgical or non-surgical, the models usage is as follows:
+**Frame classification models** are used in the step 3 of the data curation pipeline to classify a frame as either surgical or non-surgical, the models usage is as follows:
 
    ```python
    import torch
@@ -99,7 +99,7 @@ Frame classification models are used in the step 3 of the data curation pipeline
    outputs = net(img_tensor)
    ```
 
-Non-surgical object detection models are used to obliterate the non-surgical region in the surgical frames (e.g. user interface information), the models usage is as follows:
+**Non-surgical object detection models** are used to obliterate the non-surgical region in the surgical frames (e.g. user interface information), the models usage is as follows:
 
    ```python
    import torch