Update README.md

README.md

@@ -51,14 +51,16 @@ model-index:
 
 ## Overview 
 This model takes images or video frames as input, and identifies the most likely types of trash present in the scene.
-The model has been specifically built for aquatic trash, but performs almost equally well on terrestrial trash.
+The model has been specifically built for aquatic trash, but performs almost equally well on terrestrial trash. 
+Applications include automatic trash classification, ecological monitoring, and sorting at recycling plants.
 
 # Usage
 The model has been trained on 120 x 120 RGB images. To evaluate the contents of an image, you will need to pass in a tensor of shape (120,120,3). <br>
 Output consists of a 10-d tensor of class probabilities.
 
 ## Training and Classes 
-Trained for 22 epochs on 3500 data points.
+Trained for 22 epochs on 3000 data points. Model accuracies are in the sidebar.<br>
+Please read the 'Limitations' section for information on how the model was evaluated for accuracy.
 
 #### Class labels
 trash_classes = ['battery','biological','glass','cardboard','clothes','metal','paper','plastic','shoes','trash']
@@ -83,7 +85,8 @@ class_to_idx = {<br>
 
 ## Limitations
 The model has limited training data of trash in the environment. Additionally, the model overrepresents plastic and glass
-in its predictions due to sampling bias and visual similarities between plastic, glass, and other common types of trash.
-As a solution, the model is marked as 'correct' when the correct label is within the model's top N most predicted trash types. 
+in its predictions due to sampling bias and visual similarities between plastic, glass, and other common types of trash. 
+Additionally, may types of trash look visually similar or identical, even to humans.
+As a solution, the model is marked as 'correct' when the correct label is within the model's top r most predicted trash types. 
 
-n = 3 gives the most appropriate results.
+r = 3 gives the most appropriate results.