diff --git "a/S12.ipynb.ipynb" "b/S12.ipynb.ipynb"
new file mode 100644--- /dev/null
+++ "b/S12.ipynb.ipynb"
@@ -0,0 +1,423 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: torch in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 1)) (2.0.1)Note: you may need to restart the kernel to use updated packages.\n",
+      "\n",
+      "Requirement already satisfied: torchvision in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 2)) (0.15.2)\n",
+      "Requirement already satisfied: torchinfo in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 3)) (1.8.0)\n",
+      "Requirement already satisfied: tqdm in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 4)) (4.65.0)\n",
+      "Requirement already satisfied: matplotlib in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 5)) (3.7.1)\n",
+      "Requirement already satisfied: albumentations in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 6)) (1.3.1)\n",
+      "Requirement already satisfied: numpy in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 7)) (1.24.3)\n",
+      "Requirement already satisfied: opencv-python in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 8)) (4.7.0.72)\n",
+      "Requirement already satisfied: torch-lr-finder in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 9)) (0.2.1)\n",
+      "Requirement already satisfied: grad-cam in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 10)) (1.4.8)\n",
+      "Requirement already satisfied: pytorch-lightning in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 11)) (2.0.6)\n",
+      "Requirement already satisfied: torchmetrics in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 12)) (1.0.0)\n",
+      "Requirement already satisfied: pandas in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 13)) (2.0.3)\n",
+      "Requirement already satisfied: gradio in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 14)) (3.40.1)\n",
+      "Requirement already satisfied: Pillow in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 15)) (9.5.0)\n",
+      "Requirement already satisfied: filelock in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (3.12.0)\n",
+      "Requirement already satisfied: typing-extensions in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (4.6.2)\n",
+      "Requirement already satisfied: sympy in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (1.12)\n",
+      "Requirement already satisfied: networkx in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (3.1)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (3.1.2)\n",
+      "Requirement already satisfied: requests in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torchvision->-r requirements.txt (line 2)) (2.31.0)\n",
+      "Requirement already satisfied: colorama in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from tqdm->-r requirements.txt (line 4)) (0.4.6)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (1.0.7)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (4.39.4)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (1.4.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (23.1)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (3.0.9)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (2.8.2)\n",
+      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (1.11.0)\n",
+      "Requirement already satisfied: scikit-image>=0.16.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (0.21.0)\n",
+      "Requirement already satisfied: PyYAML in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (6.0)\n",
+      "Requirement already satisfied: qudida>=0.0.4 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (0.0.4)\n",
+      "Requirement already satisfied: opencv-python-headless>=4.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (4.7.0.72)\n",
+      "Requirement already satisfied: ttach in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from grad-cam->-r requirements.txt (line 10)) (0.0.3)\n",
+      "Requirement already satisfied: scikit-learn in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from grad-cam->-r requirements.txt (line 10)) (1.3.0)\n",
+      "Requirement already satisfied: fsspec[http]>2021.06.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pytorch-lightning->-r requirements.txt (line 11)) (2023.6.0)\n",
+      "Requirement already satisfied: lightning-utilities>=0.7.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pytorch-lightning->-r requirements.txt (line 11)) (0.9.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pandas->-r requirements.txt (line 13)) (2023.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pandas->-r requirements.txt (line 13)) (2023.3)\n",
+      "Requirement already satisfied: aiofiles<24.0,>=22.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (23.2.1)\n",
+      "Requirement already satisfied: aiohttp~=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (3.8.4)\n",
+      "Requirement already satisfied: altair<6.0,>=4.2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (5.0.1)\n",
+      "Requirement already satisfied: fastapi in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.101.1)\n",
+      "Requirement already satisfied: ffmpy in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.3.1)\n",
+      "Requirement already satisfied: gradio-client>=0.4.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.4.0)\n",
+      "Requirement already satisfied: httpx in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.24.1)\n",
+      "Requirement already satisfied: huggingface-hub>=0.14.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.16.4)\n",
+      "Requirement already satisfied: importlib-resources<7.0,>=1.3 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (6.0.1)\n",
+      "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.2.0)\n",
+      "Requirement already satisfied: markupsafe~=2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.1.2)\n",
+      "Requirement already satisfied: mdit-py-plugins<=0.3.3 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.3.3)\n",
+      "Requirement already satisfied: orjson~=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (3.9.4)\n",
+      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.1.1)\n",
+      "Requirement already satisfied: pydub in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.25.1)\n",
+      "Requirement already satisfied: python-multipart in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.0.6)\n",
+      "Requirement already satisfied: semantic-version~=2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.10.0)\n",
+      "Requirement already satisfied: uvicorn>=0.14.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.23.2)\n",
+      "Requirement already satisfied: websockets<12.0,>=10.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (11.0.3)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (23.1.0)\n",
+      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (3.1.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (6.0.4)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (4.0.2)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (1.9.2)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (1.3.3)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (1.3.1)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (4.19.0)\n",
+      "Requirement already satisfied: toolz in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (0.12.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from markdown-it-py[linkify]>=2.0.0->gradio->-r requirements.txt (line 14)) (0.1.2)\n",
+      "Requirement already satisfied: linkify-it-py<3,>=1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from markdown-it-py[linkify]>=2.0.0->gradio->-r requirements.txt (line 14)) (2.0.2)\n",
+      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio->-r requirements.txt (line 14)) (0.5.0)\n",
+      "Requirement already satisfied: pydantic-core==2.4.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio->-r requirements.txt (line 14)) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from python-dateutil>=2.7->matplotlib->-r requirements.txt (line 5)) (1.16.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision->-r requirements.txt (line 2)) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision->-r requirements.txt (line 2)) (2.0.2)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision->-r requirements.txt (line 2)) (2023.5.7)\n",
+      "Requirement already satisfied: imageio>=2.27 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (2.31.1)\n",
+      "Requirement already satisfied: tifffile>=2022.8.12 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (2023.4.12)\n",
+      "Requirement already satisfied: PyWavelets>=1.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (1.4.1)\n",
+      "Requirement already satisfied: lazy_loader>=0.2 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (0.2)\n",
+      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-learn->grad-cam->-r requirements.txt (line 10)) (1.3.1)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-learn->grad-cam->-r requirements.txt (line 10)) (3.2.0)\n",
+      "Requirement already satisfied: click>=7.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from uvicorn>=0.14.0->gradio->-r requirements.txt (line 14)) (8.1.6)\n",
+      "Requirement already satisfied: h11>=0.8 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from uvicorn>=0.14.0->gradio->-r requirements.txt (line 14)) (0.14.0)\n",
+      "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from fastapi->gradio->-r requirements.txt (line 14)) (0.27.0)\n",
+      "Requirement already satisfied: httpcore<0.18.0,>=0.15.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from httpx->gradio->-r requirements.txt (line 14)) (0.17.3)\n",
+      "Requirement already satisfied: sniffio in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from httpx->gradio->-r requirements.txt (line 14)) (1.3.0)\n",
+      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from sympy->torch->-r requirements.txt (line 1)) (1.3.0)\n",
+      "Requirement already satisfied: anyio<5.0,>=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio->-r requirements.txt (line 14)) (3.7.1)\n",
+      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (2023.7.1)\n",
+      "Requirement already satisfied: referencing>=0.28.4 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (0.30.2)\n",
+      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (0.9.2)\n",
+      "Requirement already satisfied: uc-micro-py in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio->-r requirements.txt (line 14)) (1.0.2)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip available: 22.3.1 -> 23.2.1\n",
+      "[notice] To update, run: C:\\Users\\SAHITHI\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "%pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\SAHITHI\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\torch_lr_finder\\lr_finder.py:5: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Device Selected: cpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import seed\n",
+    "seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import cifar10_dataset\n",
+    "batch_size = 512\n",
+    "cifar10 = cifar10_dataset(batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cifar10.show_examples()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from models.CUSTOMRESNET import Model\n",
+    "model = Model(cifar10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\SAHITHI\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\lightning_fabric\\connector.py:554: UserWarning: 16 is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n",
+      "  rank_zero_warn(\n",
+      "C:\\Users\\SAHITHI\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pytorch_lightning\\trainer\\connectors\\accelerator_connector.py:508: UserWarning: You passed `Trainer(accelerator='cpu', precision='16-mixed')` but AMP with fp16 is not supported on CPU. Using `precision='bf16-mixed'` instead.\n",
+      "  rank_zero_warn(\n",
+      "Using bfloat16 Automatic Mixed Precision (AMP)\n",
+      "Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.model_summary.ModelSummary'>]. Skipping setting a default `ModelSummary` callback.\n",
+      "GPU available: False, used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "C:\\Users\\SAHITHI\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pytorch_lightning\\trainer\\connectors\\logger_connector\\logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "  warning_cache.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils.incorrect_images import incorrect\n",
+    "wrong = incorrect(model, precision=16)\n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n",
+      "Requirement already satisfied: torch in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 1)) (2.0.1)\n",
+      "Requirement already satisfied: torchvision in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 2)) (0.15.2)\n",
+      "Requirement already satisfied: torchinfo in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 3)) (1.8.0)\n",
+      "Requirement already satisfied: tqdm in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 4)) (4.65.0)\n",
+      "Requirement already satisfied: matplotlib in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 5)) (3.7.1)\n",
+      "Requirement already satisfied: albumentations in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 6)) (1.3.1)\n",
+      "Requirement already satisfied: numpy in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 7)) (1.24.3)\n",
+      "Requirement already satisfied: opencv-python in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 8)) (4.7.0.72)\n",
+      "Requirement already satisfied: torch-lr-finder in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 9)) (0.2.1)\n",
+      "Requirement already satisfied: grad-cam in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 10)) (1.4.8)\n",
+      "Requirement already satisfied: pytorch-lightning in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 11)) (2.0.6)\n",
+      "Requirement already satisfied: torchmetrics in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 12)) (1.0.0)\n",
+      "Requirement already satisfied: pandas in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 13)) (2.0.3)\n",
+      "Requirement already satisfied: gradio in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 14)) (3.40.1)\n",
+      "Requirement already satisfied: Pillow in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from -r requirements.txt (line 15)) (9.5.0)\n",
+      "Requirement already satisfied: filelock in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (3.12.0)\n",
+      "Requirement already satisfied: typing-extensions in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (4.6.2)\n",
+      "Requirement already satisfied: sympy in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (1.12)\n",
+      "Requirement already satisfied: networkx in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (3.1)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch->-r requirements.txt (line 1)) (3.1.2)\n",
+      "Requirement already satisfied: requests in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torchvision->-r requirements.txt (line 2)) (2.31.0)\n",
+      "Requirement already satisfied: colorama in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from tqdm->-r requirements.txt (line 4)) (0.4.6)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (1.0.7)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (4.39.4)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (1.4.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (23.1)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (3.0.9)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib->-r requirements.txt (line 5)) (2.8.2)\n",
+      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (1.11.0)\n",
+      "Requirement already satisfied: scikit-image>=0.16.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (0.21.0)\n",
+      "Requirement already satisfied: PyYAML in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (6.0)\n",
+      "Requirement already satisfied: qudida>=0.0.4 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (0.0.4)\n",
+      "Requirement already satisfied: opencv-python-headless>=4.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from albumentations->-r requirements.txt (line 6)) (4.7.0.72)\n",
+      "Requirement already satisfied: ttach in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from grad-cam->-r requirements.txt (line 10)) (0.0.3)\n",
+      "Requirement already satisfied: scikit-learn in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from grad-cam->-r requirements.txt (line 10)) (1.3.0)\n",
+      "Requirement already satisfied: fsspec[http]>2021.06.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pytorch-lightning->-r requirements.txt (line 11)) (2023.6.0)\n",
+      "Requirement already satisfied: lightning-utilities>=0.7.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pytorch-lightning->-r requirements.txt (line 11)) (0.9.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pandas->-r requirements.txt (line 13)) (2023.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pandas->-r requirements.txt (line 13)) (2023.3)\n",
+      "Requirement already satisfied: aiofiles<24.0,>=22.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (23.2.1)\n",
+      "Requirement already satisfied: aiohttp~=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (3.8.4)\n",
+      "Requirement already satisfied: altair<6.0,>=4.2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (5.0.1)\n",
+      "Requirement already satisfied: fastapi in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.101.1)\n",
+      "Requirement already satisfied: ffmpy in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.3.1)\n",
+      "Requirement already satisfied: gradio-client>=0.4.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.4.0)\n",
+      "Requirement already satisfied: httpx in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.24.1)\n",
+      "Requirement already satisfied: huggingface-hub>=0.14.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.16.4)\n",
+      "Requirement already satisfied: importlib-resources<7.0,>=1.3 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (6.0.1)\n",
+      "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.2.0)\n",
+      "Requirement already satisfied: markupsafe~=2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.1.2)\n",
+      "Requirement already satisfied: mdit-py-plugins<=0.3.3 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.3.3)\n",
+      "Requirement already satisfied: orjson~=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (3.9.4)\n",
+      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.1.1)\n",
+      "Requirement already satisfied: pydub in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.25.1)\n",
+      "Requirement already satisfied: python-multipart in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.0.6)\n",
+      "Requirement already satisfied: semantic-version~=2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (2.10.0)\n",
+      "Requirement already satisfied: uvicorn>=0.14.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (0.23.2)\n",
+      "Requirement already satisfied: websockets<12.0,>=10.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from gradio->-r requirements.txt (line 14)) (11.0.3)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (23.1.0)\n",
+      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (3.1.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (6.0.4)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (4.0.2)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (1.9.2)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (1.3.3)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from aiohttp~=3.0->gradio->-r requirements.txt (line 14)) (1.3.1)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (4.19.0)\n",
+      "Requirement already satisfied: toolz in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (0.12.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from markdown-it-py[linkify]>=2.0.0->gradio->-r requirements.txt (line 14)) (0.1.2)\n",
+      "Requirement already satisfied: linkify-it-py<3,>=1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from markdown-it-py[linkify]>=2.0.0->gradio->-r requirements.txt (line 14)) (2.0.2)\n",
+      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio->-r requirements.txt (line 14)) (0.5.0)\n",
+      "Requirement already satisfied: pydantic-core==2.4.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio->-r requirements.txt (line 14)) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from python-dateutil>=2.7->matplotlib->-r requirements.txt (line 5)) (1.16.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision->-r requirements.txt (line 2)) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision->-r requirements.txt (line 2)) (2.0.2)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision->-r requirements.txt (line 2)) (2023.5.7)\n",
+      "Requirement already satisfied: imageio>=2.27 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (2.31.1)\n",
+      "Requirement already satisfied: tifffile>=2022.8.12 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (2023.4.12)\n",
+      "Requirement already satisfied: PyWavelets>=1.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (1.4.1)\n",
+      "Requirement already satisfied: lazy_loader>=0.2 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-image>=0.16.1->albumentations->-r requirements.txt (line 6)) (0.2)\n",
+      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-learn->grad-cam->-r requirements.txt (line 10)) (1.3.1)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from scikit-learn->grad-cam->-r requirements.txt (line 10)) (3.2.0)\n",
+      "Requirement already satisfied: click>=7.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from uvicorn>=0.14.0->gradio->-r requirements.txt (line 14)) (8.1.6)\n",
+      "Requirement already satisfied: h11>=0.8 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from uvicorn>=0.14.0->gradio->-r requirements.txt (line 14)) (0.14.0)\n",
+      "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from fastapi->gradio->-r requirements.txt (line 14)) (0.27.0)\n",
+      "Requirement already satisfied: httpcore<0.18.0,>=0.15.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from httpx->gradio->-r requirements.txt (line 14)) (0.17.3)\n",
+      "Requirement already satisfied: sniffio in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from httpx->gradio->-r requirements.txt (line 14)) (1.3.0)\n",
+      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from sympy->torch->-r requirements.txt (line 1)) (1.3.0)\n",
+      "Requirement already satisfied: anyio<5.0,>=3.0 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio->-r requirements.txt (line 14)) (3.7.1)\n",
+      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (2023.7.1)\n",
+      "Requirement already satisfied: referencing>=0.28.4 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (0.30.2)\n",
+      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio->-r requirements.txt (line 14)) (0.9.2)\n",
+      "Requirement already satisfied: uc-micro-py in c:\\users\\sahithi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio->-r requirements.txt (line 14)) (1.0.2)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip available: 22.3.1 -> 23.2.1\n",
+      "[notice] To update, run: C:\\Users\\SAHITHI\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n",
+      " 49%|████▉     | 98/200 [41:57<33:18, 19.59s/it]  "
+     ]
+    }
+   ],
+   "source": [
+    "wrong.execute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(model.state_dict(), 'model_weights.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "wrong.show_incorrect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "wrong.show_incorrect(cams=True, target_layer=model.network[3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}