Update README.md

README.md

@@ -38,14 +38,14 @@ This Hugging Face repository includes video storyboard classification models, fr
 </div>
 
 
-The video processing pipeline leading to the clean videos in the Surg-3M dataset is as follows:
+The data curation pipeline leading to the clean videos in the Surg-3M dataset is as follows:
 <div align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/67d9504a41d31cc626fcecc8/yj2S0GMJm2C2AYwbr1p6G.png"> </img>
 </div>
 
 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.
+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 model
+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 model
    outputs = net(img_tensor)
    ```
 
-Non-surgical object detection model
+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