Update README.md

README.md

@@ -15,6 +15,17 @@ tags:
 datasets:
 - brsdincer/garbage-collective-data-for-nature-conservation 
 - harshpanwar/aquatrash
+model-index:
+- name: Trashnet r = 1
+  results:
+  - task:
+      type: trash-classification            # Required. Example: automatic-speech-recognition
+    dataset:
+      type: aquatic-trash          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: combined-trash-images          # Required. A pretty name for the dataset. Example: Common Voice (French)
+    metrics:
+      - type: accuracy-radius         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 27.47                # Required. Example: 20.90
 ---
 # Trashnet - Trash Identification model for forensic trash cleanup
 
@@ -23,10 +34,11 @@ This model takes images or video frames as input, and identifies the most likely
 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). 
+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 3500 data points.
 
 #### Class labels
 trash_classes = ['battery','biological','glass','cardboard','clothes','metal','paper','plastic','shoes','trash']
@@ -34,19 +46,19 @@ trash_classes = ['battery','biological','glass','cardboard','clothes','metal','p
 #### 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>
-}
+    <tab>'battery':0,<br>
+    <tab>  'biological':1,<br>
+     <tab> 'glass':2,<br>
+    <tab>'brown_glass':2,<br>
+    <tab>'green_glass':2,<br>
+    <tab>'cardboard':3,<br>
+    <tab>'clothes':4,<br>
+    <tab>'metal':5,<br>
+    <tab>'paper':6,<br>
+    <tab>'plastic':7,<br>
+    <tab>'shoes':8,<br>
+    <tab>'trash':9<br>
+}<br>
 
 
 ## Limitations
@@ -54,5 +66,4 @@ The model has limited training data of trash in the environment. Additionally, t
 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.