Update README.md

README.md

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 # Scientific Context
 
-<!-- Provide a quick summary of what the model is/does. --> Pinnipeds are abundent along the entire west coast, their populations and destributions can tell us about the health of our oceans and coastlines. Studying Pinniped is important because they are seentinals of the ocean, they are indicators of a helathy ocean and can help us better understand ecoosystems and fisheries that arre critical to many communities. -->
+<!-- Provide a quick summary of what the model is/does. --> Pinnipeds are abundent along the entire west coast, their populations and destributions can tell us about the health of our oceans and coastlines. Studying Pinniped is important because they are seentinals of the ocean, they are indicators of a helathy ocean and can help us better understand ecoosystems and fisheries that arre critical to many communities.
 
 
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. --> This model detects California Sea Lions, and Northern Fur Seals, and a veriety of age classes. It is designed to help streamline population counts and help scientists study Pinniped populations. This model uses images from an aerial survey of the California coast by NOAA's Marine Mammal Lab. It uses YOLOv11 as a base model for inferences. Model weights are updated to the most recent model run. -->
+<!-- Provide a longer summary of what this model is. --> This model detects California Sea Lions, and Northern Fur Seals, and a veriety of age classes. It is designed to help streamline population counts and help scientists study Pinniped populations. This model uses images from an aerial survey of the California coast by NOAA's Marine Mammal Lab. It uses YOLOv11 as a base model for inferences. Model weights are updated to the most recent model run.
 
 ## Dataset description
 
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 ## Model Selection
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> I used Yolov11 because it was the most up to date Yolo at the time. I used object detectiono because it would allow me to both collect data no population numbers for multiple classes, but it also allows me to see destribtions on specific beaches and landforms. -->
-
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> I used Yolov11 because it was the most up to date Yolo at the time. I used object detectiono because it would allow me to both collect data no population numbers for multiple classes, but it also allows me to see destribtions on specific beaches and landforms.
 ## Model implementation
 
 '''python
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 ![F1-confidence curve](https://huggingface.co/OceanCV/Pinniped_Model/raw/main/F1_curve%20(4).png)
 
-<!-- F1-confidence curves shows us the ideal balance between precision and recall and how that changes with confidence.  My model shows decent F1 scores for CU_non_pup and ZC_non_pup around 0.3 confidence but struggles with all of the other classes. -->
+<!-- F1-confidence curves shows us the ideal balance between precision and recall and how that changes with confidence.  My model shows decent F1 scores for CU_non_pup and ZC_non_pup around 0.3 confidence but struggles with all of the other classes.
 
 ![Normalized confusion matrrix](https://huggingface.co/OceanCV/Pinniped_Model/blob/main/confusion_matrix_normalized%20(2).png)
 
-<!-- Normalized Confusion Matrix shows the percentage of true predictions against all of the other classes in the dataset. Here we can see that my model has difficulties correctly predicting some of my smaller classes as well as the pups. -->
+<!-- Normalized Confusion Matrix shows the percentage of true predictions against all of the other classes in the dataset. Here we can see that my model has difficulties correctly predicting some of my smaller classes as well as the pups.