Create README.md

README.md

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+---
+license: bsd
+language:
+- en
+metrics:
+- accuracy
+tags:
+- climate
+- trash
+- classifier
+- water
+- aquatic
+- pollution
+- environment
+datasets:
+- brsdincer/garbage-collective-data-for-nature-conservation 
+- harshpanwar/aquatrash
+---
+# Trashnet - Trash Identification model for forensic trash cleanup
+
+## 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.
+
+# 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). 
+
+## Training and Classes 
+Trained for 22 epochs on 3500 data points
+
+#### Class labels
+trash_classes = ['battery','biological','glass','cardboard','clothes','metal','paper','plastic','shoes','trash']
+
+#### Mapping common trash types from training data together
+
+class_to_idx = {<br>
+      'battery':0,<br>
+      'biological':1,<br>
+      'glass':2,<br>
+    'brown_glass':2,<br>
+    'green_glass':2,<br>
+    'cardboard':3,<br>
+    'clothes':4,<br>
+    'metal':5,<br>
+    'paper':6,<br>
+    'plastic':7,<br>
+    'shoes':8,<br>
+    'trash':9<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. 
+
+
+n = 3 gives the most appropriate results.