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app.py

@@ -1,12 +1,27 @@
 import streamlit as st
 from ultralytics import YOLO
 import tempfile
-
+import pandas as pd
 
 model = YOLO('best.pt')
 
 st.title('Spare-it Segmentation Model')
 
+# Performance table data
+st.header("Best Model Performance")
+performance_data = {
+    "Class": ["Paper Towel/Napkins/Tissue Paper", "Office Paper", "Snack or Candy Bag or Wrapper", "Metal Can", "Clean Plastic Film", "Fruits And Veggies", "Clean Cardboard", "Plastic Lid except black", "Empty Paper Bag", "Other Food or Mixed Food", "Paper Cup", "Plastic Drink Bottle", "Batteries", "Plastic Straws", "Compostable Fiber Ware", "Clear Plastic Cup", "Sandwich paper wrapper", "Filled Bag", "Wooden Coffee Stirrer or Utensil or Chopstick", "Shelf Stable Carton", "Flexible container lid / seal", "Magazines/Newspaper", "Small Paper Packets", "Plastic Cutlery", "Receipts and Thermal Paper", "Aluminum Foil", "Yogurt Tub or Container", "Cardboard Coffee Cup Sleeve", "Tea Bags", "Colored Memo Note", "Clean Paper Plate", "Glass Bottles", "Metallic Bottle Cap or Lid", "Compostable Cutlery", "Wrapping Paper", "Compostable Plastic Lid", "Plastic Milk Jug or Personal Care Bottle", "Latex Gloves", "Shredded Paper", "Refrigerated Beverage Carton", "Liquids"],
+    "Images": [906, 248, 870, 634, 427, 512, 304, 333, 487, 133, 215, 2655, 148, 184, 146, 86, 110, 63, 231, 30, 78, 26, 61, 22, 62, 451, 47, 32, 32, 41, 44, 49, 35, 37, 31, 32, 63, 17, 8],
+    "Instances": [1870, 562, 1662, 1560, 626, 928, 573, 373, 970, 196, 355, 3553, 164, 167, 196, 94, 172, 65, 655, 112, 132, 112, 72, 18, 71, 510, 51, 33, 34, 47, 54, 59, 44, 49, 39, 39, 74, 21, 8],
+    "Box(P)": [0.695, 0.652, 0.683, 0.856, 0.663, 0.71, 0.685, 0.675, 0.671, 0.628, 0.687, 0.835, 0.704, 0.747, 0.666, 0.704, 0.649, 0.705, 0.695, 0.585, 0.662, 0.643, 0.615, 0.601, 0.542, 0.757, 0.556, 0.283, 0.431, 0.326, 0.344, 0.451, 0.503, 0.354, 0.515, 0.662, 0.546, 0.603, 0.777, 0.954],
+    "Box(R)": [0.665, 0.58, 0.623, 0.922, 0.482, 0.607, 0.621, 0.589, 0.591, 0.421, 0.738, 0.859, 0.729, 0.729, 0.667, 0.682, 0.549, 0.566, 0.611, 0.463, 0.533, 0.633, 0.515, 0.507, 0.351, 0.705, 0.474, 0.354, 0.329, 0.305, 0.256, 0.617, 0.543, 0.433, 0.568, 0.617, 0.717, 0.581, 0.567, 0.357],
+    "Box(mAP50)": [0.726, 0.605, 0.615, 0.892, 0.542, 0.656, 0.626, 0.604, 0.597, 0.487, 0.687, 0.859, 0.729, 0.718, 0.686, 0.635, 0.569, 0.621, 0.651, 0.485, 0.584, 0.633, 0.511, 0.507, 0.384, 0.745, 0.524, 0.354, 0.321, 0.305, 0.257, 0.627, 0.444, 0.393, 0.568, 0.711, 0.467, 0.561, 0.744, 0.123],
+    "Mask(mAP50-95)": [0.462, 0.357, 0.411, 0.683, 0.314, 0.453, 0.391, 0.372, 0.367, 0.268, 0.437, 0.737, 0.523, 0.483, 0.437, 0.373, 0.318, 0.428, 0.435, 0.365, 0.385, 0.421, 0.321, 0.358, 0.283, 0.619, 0.351, 0.234, 0.212, 0.189, 0.147, 0.392, 0.318, 0.27, 0.391, 0.492, 0.248, 0.318, 0.587, 0.118],
+}
+
+df = pd.DataFrame(performance_data)
+st.dataframe(df)
+
 with st.expander("See Example Results"):
     st.write("Here are some example images with detections:")
     st.image('exampeimg1.jpg')

best.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:28b855cb5a55a6190548669d4738318ccabc6c2fe3d378f8390db32ba1298ae6
-size 23866989
+oid sha256:647c0ba24c39a497ead342efb3d2790b4f191d28e154f36d863ff3c01a1b6f88
+size 47572464