Update README.md

README.md

@@ -48,19 +48,8 @@ Videos 2 & 3:
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 # Model Selection
 ### YOLOv11 Object Detection Model
-I chose to use YOLOv11 to create a custom object detection model. The model was trained to detect the presence of Southern Sea Otters in water and on land. I chose an object detection model because it would allow me to add additional features to my model through supplementary code snippets. The features would produce additional visualizations to better interpret movement patterns and behaviors from the dataset. I manipulated the code [found in our textbook](https://oceancv.org/) to better identify and correctly label the otters in lower resolution cam footage, as well as in both land and water.  
-
-### Additional Features: Ultralytics Objects Counting in Regions & Heatmap
-Additionally, I utilized code from Ultralytics to produce an object counting in region video. I defined the 3 regions of interest (land, water, seclusion) through pixel coordinates over the input video, meaning the model was then able to differentiate between each zone and produce unique counts for each. I was able to manipulate the base code to track all 3 regions as once/ 
-
-I also utilized Ultralytics’ heatmap code to display a heatmap on the input video, showing which regions of the enclosure were most frequently occupied. I specifically focused the heatmap on the land zone, where the otters were spending most of their resting time. I manipulated the code to focus on the land zone, using pixel locations to create a custom region within the video.
-
+I chose to use YOLOv11 to create a custom object detection model. The model was trained to detect the presence of Southern Sea Otters in water and on land. I chose an object detection model because it would allow me to add additional features to my model through supplementary code snippets. The features would produce additional visualizations to better interpret movement patterns and behaviors from the dataset. I manipulated the code [found in our textbook](https://oceancv.org/) to better identify and correctly label the otters in lower resolution cam footage, as well as in both land and water. These are my model parameters. 
 ```python
-# Load the YOLOv11 model
-model = YOLO("yolo11n.pt")
-
-# Path to the dataset configuration YAML file
-dataset_config = '/content/Dataset/data.yaml'  # Path to the YAML file
 
 # Train the model
 results = model.train(
@@ -74,8 +63,13 @@ results = model.train(
     iou=0.5
 )
 
-print(results)
 ```
+
+### Additional Features: Ultralytics Objects Counting in Regions & Heatmap
+Additionally, I utilized code from Ultralytics to produce an object counting in region video. I defined the 3 regions of interest (land, water, seclusion) through pixel coordinates over the input video, meaning the model was then able to differentiate between each zone and produce unique counts for each. I was able to manipulate the base code to track all 3 regions at once. 
+
+I also utilized Ultralytics’ heatmap code to display a heatmap on the input video, showing which regions of the enclosure were most frequently occupied. I specifically focused the heatmap on the land zone, where the otters were spending most of their resting time. I manipulated the code to focus on the land zone, using pixel locations to create a custom region within the video.
+
 ---
 # Model Assessment
 ### Here are the metrics I used to assess the accuracy and performance of my model during training.