VGG 16 - Scratch using pytorch for Image classification.ipynb

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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99afa48c-a2b3-41ac-a65b-bff35b1dd9df",
   "metadata": {
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   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms as T\n",
    "from torchvision.io import read_image\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import time\n",
    "from tqdm.notebook import trange, tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df13bef-7d2f-47cf-97cd-19e779566ba3",
   "metadata": {
    "id": "2df13bef-7d2f-47cf-97cd-19e779566ba3"
   },
   "source": [
    "### VGG 16 architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c7582d5-8d8f-48df-9c1e-c75bd1acb86d",
   "metadata": {
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   "source": [
    "class VGG16(nn.Module):\n",
    "    def __init__(self, num_classes = 1000):\n",
    "        super().__init__()\n",
    "        # input size => output size\n",
    "        self.layers = nn.Sequential(\n",
    "            # 3,300,300 => 64,300,300\n",
    "            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 64,300,300 => 64,300,300\n",
    "            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 64,300,300 => 64,150,150\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # 64,150,150 => 128,150,150\n",
    "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 128,150,150 => 128,150,150\n",
    "            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 128,150,150 => 128,75,75\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # 128,75,75 => 256,75,75\n",
    "            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 256,75,75 => 256,75,75\n",
    "            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 256,75,75 => 256,75,75\n",
    "            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 256,75,75 => 256,38,38\n",
    "            nn.AdaptiveMaxPool2d(output_size=38),\n",
    "\n",
    "            # nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # 256,38,38 => 512,38,38\n",
    "            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 512,38,38 => 512,38,38\n",
    "            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 512,38,38 => 512,38,38\n",
    "            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 512,38,38 => 512,19,19\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # 512,38,38 => 512,10,10\n",
    "            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 512,10,10 => 512,10,10\n",
    "            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 512,10,10 => 512,10,10\n",
    "            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            # 512,10,10 => 512,10,10\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "            )\n",
    "\n",
    "        self.fc = nn.Sequential(nn.Linear(512*9*9, 4096), nn.ReLU(), nn.Linear(4096, 4096), nn.Linear(4096, num_classes))\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        convolved = self.layers(x)\n",
    "        bs,_,h,w = convolved.shape\n",
    "        convolved = convolved.view(bs, -1)\n",
    "        return self.fc(convolved)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb190c43-a203-4e45-8d38-48a44cce1daf",
   "metadata": {
    "id": "fb190c43-a203-4e45-8d38-48a44cce1daf"
   },
   "source": [
    "### Things to know aboubt size/shape of the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbdfe020-063d-47b8-87d1-2f761fcd0585",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1728974295753,
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     "user_tz": -330
    },
    "id": "cbdfe020-063d-47b8-87d1-2f761fcd0585"
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   "outputs": [],
   "source": [
    "# Normally image read as (Height, Width, Channels) in cv2, plt & PIL\n",
    "# plt.imread(\"D:/Learning/ImageClassification/id_cards/2.jpg\").shape\n",
    "# Output: (181, 278, 3)\n",
    "# In torch, it is read as (Channels, Height, Width) - channels in the first dimension\n",
    "# Output: (3, 181, 278)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d94d61f-72ef-404c-bcf4-3542ab850d48",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1728974296680,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "1d94d61f-72ef-404c-bcf4-3542ab850d48",
    "outputId": "086139ec-e3d9-455d-8f58-0c82011c41ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 278, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imread(\"/content/drive/MyDrive/Colab Notebooks/dataset/ImageClassification/train/id_cards/2.jpg\").shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed819226-4141-4605-8c0e-857cb3da03e9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 564,
     "status": "ok",
     "timestamp": 1728974310397,
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    "id": "ed819226-4141-4605-8c0e-857cb3da03e9",
    "outputId": "a18ab953-a53a-4d2b-aa54-644239f3ef3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 181, 278])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_image(\"/content/drive/MyDrive/Colab Notebooks/dataset/ImageClassification/train/id_cards/2.jpg\").shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd73b47e-8b8f-4d0c-95fe-0cc943110b14",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1728974311094,
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      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "dd73b47e-8b8f-4d0c-95fe-0cc943110b14"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b4719d9b-87b7-45b0-b73b-56b64bdac5c9",
   "metadata": {
    "id": "b4719d9b-87b7-45b0-b73b-56b64bdac5c9"
   },
   "source": [
    "### Transform input image to the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b66b9bb6-6f05-4c3a-af2e-b7f26bad1f6a",
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1728974311848,
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      "displayName": "Sriram Appusamy",
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     },
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    },
    "id": "b66b9bb6-6f05-4c3a-af2e-b7f26bad1f6a"
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   "outputs": [],
   "source": [
    "# a simple transformation without any augmentation\n",
    "# adding augmentation techniques like Horizontal flip, vertical flip, random crop, random rotation, color jtter give better robustness of the model\n",
    "transforms = T.Compose([T.Resize(size= (300,300)),\n",
    "                       T.ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb37d121-8cfd-495a-b36b-f9184295cfb0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1728974312594,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
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    },
    "id": "cb37d121-8cfd-495a-b36b-f9184295cfb0",
    "outputId": "64e56b97-5961-463c-8703-40b196d85c16"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Compose(\n",
       "    Resize(size=(300, 300), interpolation=bilinear, max_size=None, antialias=True)\n",
       "    ToTensor()\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f774020e-49b8-4006-a275-6fe8ba3ccedb",
   "metadata": {
    "id": "f774020e-49b8-4006-a275-6fe8ba3ccedb"
   },
   "source": [
    "### Dataset for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa232f33-5664-49e8-99a5-f78ce2b21537",
   "metadata": {
    "executionInfo": {
     "elapsed": 743,
     "status": "ok",
     "timestamp": 1728974470398,
     "user": {
      "displayName": "Sriram Appusamy",
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     },
     "user_tz": -330
    },
    "id": "aa232f33-5664-49e8-99a5-f78ce2b21537"
   },
   "outputs": [],
   "source": [
    "ds = ImageFolder(\"/content/drive/MyDrive/Colab Notebooks/dataset/ImageClassification/train/\",\n",
    "                transform = transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb321893-3ddc-494e-bda4-472e8e398ebb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
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      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
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    "outputId": "cbf41fc7-694f-4bdf-8c2b-9e6907e86810"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset ImageFolder\n",
       "    Number of datapoints: 180\n",
       "    Root location: /content/drive/MyDrive/Colab Notebooks/dataset/ImageClassification/train/\n",
       "    StandardTransform\n",
       "Transform: Compose(\n",
       "               Resize(size=(300, 300), interpolation=bilinear, max_size=None, antialias=True)\n",
       "               ToTensor()\n",
       "           )"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d53edf99-5eb1-401e-a3eb-0058f8eab0bf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1728974316532,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
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    "id": "d53edf99-5eb1-401e-a3eb-0058f8eab0bf",
    "outputId": "00415a1e-5862-404c-b976-1ad9aeb01711"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id_cards', 'license_plate']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2246b69e-357e-40d4-ae5c-42120b5a790e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1728974318596,
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      "displayName": "Sriram Appusamy",
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    "id": "2246b69e-357e-40d4-ae5c-42120b5a790e",
    "outputId": "6a91c260-740f-419e-ef2c-03ad3e93e0b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_classes = len(ds.classes)\n",
    "num_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9119ab-868b-425a-b1e7-b6b977674ce2",
   "metadata": {
    "id": "dd9119ab-868b-425a-b1e7-b6b977674ce2"
   },
   "source": [
    "### VGG network creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "286fc27a-defb-4ce9-beef-c0162dbb9223",
   "metadata": {
    "executionInfo": {
     "elapsed": 2782,
     "status": "ok",
     "timestamp": 1728974774611,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "286fc27a-defb-4ce9-beef-c0162dbb9223"
   },
   "outputs": [],
   "source": [
    "vgg = VGG16(num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9SEmahfI2rlZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1728974774612,
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      "displayName": "Sriram Appusamy",
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     },
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    },
    "id": "9SEmahfI2rlZ",
    "outputId": "750e8020-0141-454a-d731-c024f796ec38"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device='cpu'\n",
    "if torch.cuda.is_available():\n",
    "    device = 'cuda'\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d08ececc-3c0b-46b7-9e03-d8e72048bb0e",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1728974774612,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "d08ececc-3c0b-46b7-9e03-d8e72048bb0e"
   },
   "outputs": [],
   "source": [
    "vgg = vgg.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "v8jiCuNJ23zZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1728974539077,
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      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Oct 15 06:42:18 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   73C    P0              29W /  70W |   6137MiB / 15360MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3549b9ac-541f-43ee-bde8-72499abff36f",
   "metadata": {
    "id": "3549b9ac-541f-43ee-bde8-72499abff36f"
   },
   "source": [
    "### Create dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5a4d2dad-f25a-4cc8-b9cf-0eec57ac275a",
   "metadata": {
    "executionInfo": {
     "elapsed": 693,
     "status": "ok",
     "timestamp": 1728974778388,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "5a4d2dad-f25a-4cc8-b9cf-0eec57ac275a"
   },
   "outputs": [],
   "source": [
    "dataloader = DataLoader(ds, batch_size=8, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b9d49b-66f0-4dac-83a9-20b988b9b189",
   "metadata": {
    "id": "d7b9d49b-66f0-4dac-83a9-20b988b9b189"
   },
   "source": [
    "### Loss function & optimizer setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ef7133ad-43cc-40ed-9c43-3603a365e8bf",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1728974778844,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "ef7133ad-43cc-40ed-9c43-3603a365e8bf"
   },
   "outputs": [],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(vgg.parameters(), lr = 1.,momentum=0.9, weight_decay=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554df5a0-b311-468b-8052-351f1bc25e28",
   "metadata": {
    "id": "554df5a0-b311-468b-8052-351f1bc25e28"
   },
   "source": [
    "### Training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e3624abb-67af-447b-8518-a10b71070295",
   "metadata": {
    "executionInfo": {
     "elapsed": 615,
     "status": "ok",
     "timestamp": 1728974782920,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "e3624abb-67af-447b-8518-a10b71070295"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 20"
   ]
  },
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    {
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     "metadata": {},
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   ],
   "source": [
    "etl = trange(EPOCHS)\n",
    "for epoch in etl:\n",
    "    epoch_trn_losses = []\n",
    "    t = tqdm(dataloader, total = len(dataloader))\n",
    "    for xb, yb in t:\n",
    "        xb = xb.to(device)\n",
    "        yb = yb.to(device)\n",
    "        logits = vgg(xb)\n",
    "        loss = loss_fn(logits, yb)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "        t.set_postfix(loss = loss.item())\n",
    "\n",
    "    epoch_trn_losses.append(loss.item())\n",
    "    epoch_trn_loss = np.array(epoch_trn_losses).mean()\n",
    "    etl.set_postfix(epoch_loss = epoch_trn_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_Qxx26li32N5",
   "metadata": {
    "id": "_Qxx26li32N5"
   },
   "source": [
    "### Save trained weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2vilkSRE30-r",
   "metadata": {
    "executionInfo": {
     "elapsed": 5993,
     "status": "ok",
     "timestamp": 1728973962268,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "2vilkSRE30-r"
   },
   "outputs": [],
   "source": [
    "torch.save(vgg.state_dict(), \"/content/drive/MyDrive/Colab Notebooks/models/vgg16-classification.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "WqdbOCJ73xAo",
   "metadata": {
    "id": "WqdbOCJ73xAo"
   },
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "thr5I1fi3oce",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 569,
     "status": "ok",
     "timestamp": 1728974985702,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "thr5I1fi3oce",
    "outputId": "39ae0fa0-268b-4451-b3b0-b12cbcbdaa8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG16(\n",
       "  (layers): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU()\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU()\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU()\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU()\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU()\n",
       "    (16): AdaptiveMaxPool2d(output_size=38)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU()\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU()\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU()\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU()\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU()\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU()\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (fc): Sequential(\n",
       "    (0): Linear(in_features=41472, out_features=4096, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (3): Linear(in_features=4096, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "02cr7srh5PrB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1728974986428,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "02cr7srh5PrB",
    "outputId": "a2836845-16d3-4890-e23c-d83789cddf45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id_cards', 'license_plate']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "Zrwr4qvD4WYo",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1728974987211,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "Zrwr4qvD4WYo"
   },
   "outputs": [],
   "source": [
    "test_img_path = \"/content/drive/MyDrive/Colab Notebooks/dataset/ImageClassification/test/license_plate/100.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "29c99b8a-310a-40f1-a14b-ec2e63e2763b",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1728974988528,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "29c99b8a-310a-40f1-a14b-ec2e63e2763b"
   },
   "outputs": [],
   "source": [
    "img = Image.open(test_img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "iO3Fruio8MXb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 211
    },
    "executionInfo": {
     "elapsed": 1187,
     "status": "ok",
     "timestamp": 1728975000979,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "iO3Fruio8MXb",
    "outputId": "f35c275a-ca39-417e-a1bf-0d9ac034c4f1"
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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/iRLL/wAJHEm/5Ftxt/Etn+VfG4HCrEV3CWx6FSfLG6Ohl+J2kp9y1vG/4Ci/+zUsPxQ0Z32Pb3kX+1tVtv5NTNO8A6Jc6bayypK0ssKSM3mFRlgDwOw5qj4m8C6Tp+hXV7aeessC7/mk3A8jIIPse1egqeWyn7KzvexlzVUr6HoNjqFpqdil3aSrLE33WX9QfQ+1cT4w8O6DDdy6xqeoXMTzsFWOPaxcqAMKCM9B61U+FVy+NSt9/wC6Xy3Vfc5BP4gD8hWV8UJXfxHbxb/kW1DKvZcs2T+g/IVOGwcqOPdKEmlb5200HOfNT5mjbf4qaZ/Bp95/wLYv9TXL6vrXhnXdS+13FpqdrK3yyNbtH8/+0Qw6/wCFdjo/w50G60m1uLhJ5ZZYUkkbzivLANwB25qh4t8A6Ppnhu6vbFJ4pYFDLukLBssAQQfY11YetgKVflp3Um7X/pmco1XG7tY67wgtinh+3TT9QnvLXny2mxuT/YIAGMeh/liqXjq10m50mJtY1Ce2tI23LHDtzI5GBwVOSBn8zXN/CWaXGq2+791+7dV9zuBP4gD8hT/iPpGt6trFp9h0+ee1gt/laNeN7Md344C1yLD+zzJqU7db6X/yuac16Oxk6D4r8N+GftH2Gy1OV58bpJvLzgdAMEYHeptb8Y+GfE0dvb6haajF5Um5ZIdmVzwRyTx07dhW7pfwy0f+zbd75Lprpow0377btcjJUAdMHj8KzPF/gfRNG8N3GoWnnxSxMm3dJuDZYLgg/X9K7o18DUxKs3z3tf8ADuZ8tRR6WOn8E6VpNlYy3Gj6hPc2tzhtsjLwV46BQQecEH0Fa2ta5p+h2n2i+l2pu2qq/Mzn0Ud64P4SSS+XqsO/91+5ZV9zvBP5AfkKxfijeO/ifyXf5La3G1ewLZJP48fkK45YD2+YulUk2lrfrayNPactK6Oll+K2mJJ8mn3jf9+//iqWD4q6S8myWyvok/vbUbb+TZp9n8NdB+yxfaEnll2jc3nFck8nAGMCsLxr4O0bRtG+22XmrL5yR7Wk3A5z69OmfwrenSyypNUYxd3p/WpDdZK90em2GoWmp2kV3aSrLby/dZf5EdQfY0+5P7v/AIFXD/Cpn/sK9/ufavl/2flGa7O8P7tP97/GuCOGjRxyox6Nf5mrnzU7+RFmgmo80ma+xPPJM0hNNJoJpgNY15/49nuNQu7LR7GJp7hm3eXH1cngAD869R0vRrjVp9sXyp/E3YV5v8QvCWu6B4vfUbaK6e3+RrW7jXcFIX5skZ2kNnqMYNVFageY7G/iRs9+39KK27fW7W0gS3k0aymeMbTJJuDN7nmitw0PRbP4LXrwea+sacybfuwMXP8AIVu6J8O9PtZ0e43XTr/z0+YfTb0xWFYajcWUiPE7V22i+Krd9iXabX/vL/UVzOVwO4t4Le8tPskqfJ/D/wDWrm9W8PXFn88PzxV1Fg9vdR77eVWT/ZrSx8nzUJLYR5Ow2Uwmu/1Pw1b3m54vkeuO1DSLqwf5omoaGZ5NITTWpjNWU9gRug1xfjrwvcayIr2y+a6iXY0f3d6dRjtkEn8/auvjb92n+6P5VyHjfxFqfh97V7JIvKl37vMj3cjHfPof0r47CKqsV+5tfXfY9GfLye8cRHL40sI0t7ddWiSL5VVYWIX2HB4qDUbjxdeWMqXyam9oq7pPMhZEwOcngD3rvNL+IOkvpsX9oXHlXW3bIqwvjPqMAjBqt4i8b6DeeH720t7hpbieEpGvkuvJGMksAAK9qFev7VJ0Fvvb8djncY2+IzfhOf8ATtS/65x/zP8AjVX4oD/iprd/+nNP/Q3q18Kkf7dqD/weWi+2ckgfpUfxTtn/ALY0+72fumt2Td7qxJH5OP1oulmj9Bf8uT0vw+f+Kf03/r1h/wDQBWZ48P8AxRupf9cx/wChrXPaF8RdHstDsre78+KWCFImXy9w+UBcgjscVU8VePtJ1bw/d2Nj57Sz4Vd0e0LhgSSSfavKp4DErGKfI7c2/S1zd1Yez36DfhL/AK/Vf92H+b16jXmPwnt5Uj1W42fumaNFb1K7iR+G4fnUXxB8Qa3pOvxRWV7LBatbhlVVGCckNyR16fpW2NwssVj5U4NbLf0Qqc1CkmzoviBJraWll/Yn2zf5j+Z9kVs4wMZ2+9eV61N4me3RNY/tb7Pu+X7T5gjY/jwTXpNp8TNB+yRfaJp1l8seYvklucc8jg81g+NfGmk6zoT6fZeezyyIzM0e0AKc9+pP9a68uVehKNGVHS/xfqZ1eWV5c3yNv4Xx2SeHpXt9zXDTbbndjggDaF9sHP1Jrkvie9k/idPs7t9qWELcdNvquPfaefbHvXT/AAttpYfD11cOvyT3BaP3CqFz+eR+FcT4x323j28llRtnnRyr/tIFXp9cEfnV4aN8yqO+y/y0FP8Agobbf8JukCJb/wBurEq/Kq+bjHbHtWdrEmvP5Sa2+o/3o1u9+M99u7jP09a9PHxK8Pf9PS/xf6n/AAOK47xx4qstfjtYrFJdkbF2aRQvJGAAM/WuvDV68qyUqKiu5nOMVHSR3/gOGyh8KWn2LcyS5aRpMZ8zOGzj3GB7AVs3zfvIvx/pWR4JtJdP8KWUUqbXZS7K3VdzEj9CKv3z/v0/3f8AP8q8egubMm99WdEtKIZoBqBTWzpmhX2ofcTan95q+oRxGcod5PkrpdF8LS3my4u/ki/u9zXR6X4btNP+dl82X+81bVaxj3JIbe2itYFiiXai9BTbi3iuY9kqK1VdS1vT9KTdd3Co393qfyrz/XfiBcXO+Kx/cRfd3fxH8e1EmgR1EmjeHFkYTfZfM/i6UV5FJcXEkjO0zZJyetFZXRRUWpEaowKUChWA1rDW7vT5N9vK6/7rV22l/EXIVL6Ld/tLwa81xTt1O4HvNhr2mah/x73SFv7rcGtCSGKdNrorLXz7FeeTvld2WKJdzbWrb0zxnrFtAj2lvdPE3zL5jcEeoDN0qlLoTY9E1Pwlb3Pz2/yN/d/+vXJX3hy+spPub6gb4jeJv4NMg/8AHf8A4uoW+Ivil/8AmGWv/fsf/HKloaJxdpawIkqMrqu37pbp9Aao6nPo+p2j2l7vlib/AKZv8vuDjINNfxnrc3+t0ez/AOAxr/8AHaF8Q7/9bpP/AHysa/8AtavJeUUnP2ik110Zv9YdrWORm8J+Ft//ACFbyL/ZaP8ArspF8JeFvM+fU7yX/dXj8whrqrqdEj3p4cl2f3pGCj/0bWQNVvod/wBn0ewX5t3+sT/45XVHC1P+fr/D/IjnX8ptaVcaJo1olpZI0UX3v9TJlj6kkZJqTUrvRNWtHtL1Glib+HyZMqfUEDIPuKwTruvfwaZYJ/wKL/4uo/7Z8R/8+ln/AMBaH+rVzPJoOftOd3731NPrLtaxVk8H+E3+5caiv+yqt/VKdD4S8Ip87y6jL/ssr/8AsqA1YOr+Jv4LezX/AIFB/jSjWvE3/PKz/wC+rf8Axrp+pVLfxX96/wAiPaL+VHR2WqaPp9olvaI8Vuvyqq28nH/jvP1qtrEmg65afZ72KWVFbdG32eVXQ+oIHFYx1zxT/csF/wB77N/jSjXfE3/Th/31a1yrJaal7RTd+99S/rDtayM9/Bvhl/uXGpr/ANsz/wDEVJB4Q8LQSb3/ALRn/wBlo3UN/wB8oD+tXf8AhIPE39+w/wC+rSrEPizWE/4+Lewlf/r6tUrs+qTtb2j/AA/yI9ouyNmHVtPtoEiiiliiiXaqravhQOwGKxdbPhnX/k1BJfNj+VWWN0kUHnrjkfXIpz+M9T/59LBf+360qt/bl3e3e9LHTpbhvl/4+rRnbGeOBk1yRymnCXtIyafe5ft29GkZa+DvC00myK41Nn/ur/8AsVo6d4R0GynSVLG8ndfmXzo2YZ/3QoB/Grkl1renx/a/7MgtUX5vM8yBAn1baAKpyeMde/g1CzX/ALiFt/jXT9UlNWdV/gRzpdEdbFLK/wBy0uv+/Zq9B4W1PUJ/NeLyk/2vxrgR418Qp/zGbX/Z/wCJlbr/AFpD478R/wDQeg/8GkH+NThsrpUJ+0je4TruSsz2fS/B9pZfPcfvX/St8tBZwZZkiiX6AV89Q+OPEc8ixRawtxLI2Fjj1SFmPsoDDJ9qlTxDd6tBvuLidnVjG3mMW5HXrXo7GJ7DqXjfSdP3Ksv2h/7sfT864zVfiBqF58tv+4i/2ev51x9BFQ5NjHz3Us0m+V2Z2/vNUBNP2UeXSuMr5oqbyqKLsViz9koNq9bnkUnkUDMP7NSfZ62jBTTDQBzuoQf8Sm6T+9Cf0rj/ABM2/WXl/wCesMMi/QxqePbNeiX8P7h/9rK/mDXnWuf8wqV/+fGNfxVmX/2Wrp7kyM2C0uLrf9nt2l8pfMk2ru2gdScdBUJFesSXcVlqUtjY6Ppa28rQxSNu2uwlxglQOVycdarQC01CO6sZdE0mB2t7ry2gw0imJtmSNoxycg+1ac5Njzex0+71Of7PaW7Ty/e2qu48Y5xXZeEPCN9D4ktJdT0+WK3ibdubGMgHb+tclo2u3ehX326x8rzfLMf7xdwwcdvqBXQH4l+IXj2f6Hs/64//AGVKfM9ENWPaL/7O9pKkqKqbfvdlr5qMiff2bUb5lWvYlvriy8CvrGtvuuJV8yGDlR83Eakdyevtn2rxZmrOjG1xyYrf7lXrDRdQ1Pf9hsZ59v3vLjLbfrUnh3T01bxBZae77UnmEbN3x1OPfGa9P8XeMn8Hz2+iaPYwL5UIkZpFOxQcgKFBGTxkk+tattOwkeT3mn3Gnz+Vd2ksEv8AdkjKn9R0qpt/2K9dtPFOj+NvDd1Y+I3s7C7X/Vs0gQZI4kTccgg9Rk/riuM+H+jRa54ot4rhN0USmeRf723AA+m4jPtmmn3CxR0zwhrerQfaLHT5ZYufmbaobGM4yeev86y7mxuNPn+z3dvLBKv3o5Iyp+uD2969L8WfEjUNM1240zSUgit7RvKZpI9xcjGcDIAUdMe3Wr11eW/j/wCG91qFxbxRahY723L2dFDHaTyFZSMg+vsDS5n2Cx5DU1tZXF7dxW9pE0ssrbY1Xq2ahIr1nS7Ox+HPhf8AtbUIvN1i5XbHH3UkZEY9AOrH/wCtlt2C1zyq7s7iyne3u7doJV+9HIpU/kf51v8AgW9htfFdlFcbVink8tmb1wxX8CePxrK1bWb7XNSe+1CXzZW+VfRByQqjsB/Wu+8C2tjo3grUvFF3bpPLtk8tdv8AAvy7R6FmB5+lKXw6gj1DURbvptxFcRLLbtGVZdu4uMHjA68dhXzE42b0dGV1+Xay8r7EetddZfEjWba/S4dIGtd3zWyx/dTuFP3gce/4VpfFqxt4dW0/UIkVXuY3Wb/a24wT74bH4D0rOnDkevUbdzg2trhLRLt4m+ztIY1k2/IxHOM+uDV4+G9W8yVHt1V4rX7W26Rf9X69evt1rW0nXbT+xrfw9r0Lpp/2jz/PVTlIyGbgAEnLfxDsxrvr2TwskmsfaJpd8FnHaXa7X+WNvuAYHJOe1auTQkrnk+gxed4g02L/AKeI/wAgwJ/QV3Xh1ftNj9o/56s8v/fTE1yWntaJ4rluNP3fYoFnlh3ZzhYWI689cdea7zwvBs0a3/64p+oyf51nN3Gi6LenfZqvbaTbUWKKQt6X7PVz5Kb5kX99f++qasBU+z0VY8+L/nqv/fQoqRlddZl/55LT/wC2X/55L/31UX2RP9qlFon+1UgSjVn/AOeX/j1B1X/pl/49TRZUhs0qhEVxfedHs8rb8wrgdfi2abpv+zJdRflIGH6PXey2v7t3/wCBf1rjPEEf/Epl/wCmWpHb/wADjB/mhqqe5LOm/tKxstWstQ1D7L5Uum20qrJCGkeQEgbGPTHUn2FWrW8tLnxlsS4s/N865gaOCEK7IV3K5YfeHy8+9Lo/z6alvaeIdEuorO33M0lnvKIvdjv6CpU1JE+f/hI9CV/4WXTz8v0+eqA8dcbJNn935a6HwZoX/CQeILe3dP8AR4v3tx/uLjj8TgfjWbrNtb22rXEVpfRXkX3lnjXaGyMnjJ6Hj8K9M8NQw+DPAVxrd2n+l3iiRVbq2eI1/HO78fatG9BIwfilr32nUotHt3/dWfzSenmHoPwX/wBCNcJbW73s/lJt/vfN04qO5uZbqeW4l+aWVi0jN/ETyT+ZqFZnSTej7X/vLnP50ctlZCudP4NV7bx1pSP99bra34g113i/QX8R/E+LTEuPI82xDbtu77u49Mj0rh/B03/FZaO7v/y9J831IFdL8VmeHxtE6blf7HH8y5X+KQcGhrUZrf8ACnbj/oML/wCA5/8Aiq5TwTrMXhnxWkt2/wDo7b4JmXPygkfN7gFR+Ga674PzzTXesebLKyLHD95i3Uv615RcSf6XK/8AekNCu7pgeu6/8N/+Eg1V9Y0fU7Xyrv8AeMrZYZPUqy5BB61f1PSrfwT8MNStEl3SyqVaTbtLySYXgey/oteX+ENL1DXdZi0+0uJ4ImbdcSRyFQiDqxwcH0HuRXU/FHXIvPtPDlj/AMe+nqGk2t/HjCr/AMBX9W9qlp7DuZvw40dNW8VpLKm63sV89v8AfzhB+fP/AAGux8Y+Btb8Tas92l9arFEvl2sDbsY4JJIHDE/XoPSs74WyfYvDev6gibriJj8v97YhYD82NefP4h1ia/8Atz6tdfat27zFmK4+g6Y9sYp6t6B0G6vo2oaFfPaahb+VL95f4g46AqRwRXe+A9V0zU/C914U1OVYHl3rFuYLvDZPyk8bgxJx6Y98O+Jkn23wh4d1C4RVvZcbvl5+aPc4+m4LXl5/26fxIWx6rp/wnSG+ilvdTWe3Vt3lrDtL45APPA9etYXxU1BL3xJFYo/yWcO1v99uSPyC1qeErGLwn4euPFOrJ+9aPy7SNuuD0wOxcgfQCvN7y8lvbu4vrh90s7GST6k5/KkruWodDvdE1u08R6zpVpceHlunisxbSfd2Lhh8+P7oXPXuxHPFdOL+x1CfWkTwzLLtj8xmkhCi82HgDI6g9M1z+gyS+H/Emj6PYoiPfWsct/uXcchZGwpP3cjAP0GMc5s61rfiPRtNtHlvrO8vWvNyx2i7g0KoxZWAAJGec9sCk99BnF208V1fa7fW9utrE1q6xwL/AAb3RAB+BP613sBeGBIkfaiqF+X2FcFo5e6gvZZfme8vLaNm/vbmZ2/9Ar0SMJ5dRMEMMkv99v8AvqkO/wDvtVjy6d5D/wByosUVNtNK1adUT77ov+98tVnubRPv3EH/AH8X/GmgGbaKZ/aOn/8AP7B/38FFL3gKY1PXvL+e3i/75p661raf8uK/98n/ABrol2VMqpQBzo8Q6mn39P8A5/4UDxPcfx6Y3/fVdKY0pphT+5T0Fqc7/wAJT/A+nz/N9Kw9cXfpuq/9us6/nIh/9CFd2YIv7i1yeqWiPPd28vyxT2/keZtLCJ1dXQsBzt+XHGT81EXqhdDlNE8S6n4fkl/s94l8/Hmbow/TOOvTrXpVt4ui0+N77UfFenX0SxlltLSFfMZz0HHIx7/jivP08Jo//Mb07/gSzf8AxFPHg5P49e07/vmb/wCIrZpMRj2UsU2s2733/HvLcI1x6YZhu/QmvYvEVz4J8TQW8N34oiit4NzLFDcIqliAATkHoOB/vGvMpvCkUP8AzGbWX5d37iGRip9MMB+YqW38HRTWiTPr1nE7L/q2hmYr7EhMUPV3uB0z+Gvhn/0NP/k0n/xNeZ6gtpDqVxFaP5tuszrDJ/fQEhT+I5rpD4MT/oN6c3/f5f8A2nT08EWj/f8AEdhE/wDd+zzN+uwU7ruFjlLed7aeKWJ9ssTCSNl/hK8g/nzXrH/CW+DvF9jb/wDCTRNZ3sHy+ZGr4PrtZc8H+6w4P51yJ8E2ifc8R2bf9u8y/wDstNHg5P8AoPWf/fmb/wCIobXcDqNQ8aeHPDmjXGmeEYma4n+WS7ZWXbkY3ZbkkDoMbR+h8qLIn3P+A11//CGp/wBBu1/8B5v/AIimf8IOn/QYi/4Dazf/ABFCaWoam7B400Hwt4US08MpK2pXK/vp549pQ+p7EjPyqMgdT7+dtO7yO7uzOzFmZupJPJPqa62PwFF/HrH/AHzYzf4U7/hAov4NTb/wBm/+JoTiFmQeCfGH/CLX0vmxNLp9zhZo1++pGcMueCeSCD1H0rpvtPwuhk+2xJOz7t32Ty5NmfTaRjHtnb+FYX/CA/8AUQf/AMAZv/iaB4Cf/n+l/wDAGb/4mhtdw1KPi/xXN4p1JJdnlWsClbePuucZLe5wPbgfjF4SvPD1lqvna9FPLFEu6Haodcj++vU9sdvWtT/hAn/5+5/9n/QZf8KjbwK//PWf/wAA5v6ikmrWAz/Ffiq78U33nOnlWsXy28H93/ab1Y+vboPU4MEyQ3cUrp5qLIGaP++AQSPxHH411x8C/wDTxef+AL/1xSL4H/1u/wC3s/8AyzZbfaPxBz/MU+aKCzG2fiXT5vHt3rGoeatlKska/KWKgqFUYHI+XPToTVifVfB2jWlxN4ciun1Bo2ijaRWATcMFucZIB4qt/wAIRs+/cXX/AICr/V6b/wAIYiSf8fF1/wCA8X/x2lePcNSPw3D/AKJp6bP9bqDyfhHGMfq5rpGg1t5H2ahLFFuO1V+XaO3SodM0n7FPZf8APK2WTy9zKzMXPJIUkLwAMZ/hroQyVlJ3dxmGdL1Z/v6tdf8Afxv8ab/wjsr/AOtvpW/4Ef61vlqT5/7jUrhYwv8AhF7f+N2b/gVKPDlin8FbmyX/AJ4t/wB8mkMFx/zyl/75NO7Cxif2DY/88lorY+y3H/Pu36UUrjsdePD2mf8AQTb/AL5p48PWP8Grf99Kf8K84GrXf/PVqVdXvv8Anq1Tco9IHhi3/g1iL/v2aU+GU/6C0X/fv/69ecDXb7/n4apV8Sagn/Lw1O4j0A+Fv+onB/3yarS+DHeTempwK/8Ae+ZTXFf8JVqf/PxS/wDCVah/z1Wi4HYHwVqD/c16L/v4/wDhUT+BdWf/AJj0H/f5/wDCuXHjHUP76/8AfNB8Z6h/fSi+oHRt4A1b/oY4P+/z/wCFR/8ACu9W/wChmi/8CpK58+Mb7/Z/Wmf8Jjff7NO7A6I/DrWP+hpi/wDAySlHw+1v/oaYv/AyWua/4TG+/uL+tMbxfd/7NNsDqD8P9b/j8Vwf+BUlIfh9q3/Q1wf+BT1yx8WXf9yk/wCEru6AOo/4Vzffx+K4P/AiSmf8K5uP4/FcH/fx65g+Krv/AGab/wAJRd0XA6wfD3+/4ri/8fanf8IBb/x+Ll/4DGzVyI8S3dKPEd3U38gOu/4QPTP4/FbN/wBu7f404eB9E/j8R3T/AO7b/wD1647/AISK7/v0v9vXf9+i4HZDwZ4c/j1i+b/dhVf608eEvC3/AD/as3/fC1xX9u3v/PV6adau/wDnq1DA7oeFvCyf9BRv+2wX+lA0Hwyn/Lpft/vXR/oK4X+1Lt/+WrfrUf2+4f8A5av/AN9UAd8NM8PJ9zT2/wCBXT/40hh0RP8AmHwf8CkZv/Zq4E3Mv99v++qTzn/v0agd99r0mH7lpZr/AMB/xpp1mxT7nkL/ALsY/wAK4PzKN1PUDt28QWn8D/8AfK1A/iK3/wBquQDUbqVwOtXxLbp/yy3f72f6GkbxTF/BZRf98n+prk91LupXGdX/AMJV/wBOMX/fuiuVzRRZgVv79Nf76f73+FFFAl1EH8VI1FFSt/mAh/jpB/BRRVx3AU/6uox/n9KKKaJGmkaiiq6AMP8ArKRv4P8AdoopdgewN/7NRRRTY+oykNFFP7IiRP8AV/nS0UUmUPp0dFFT2AlX+OgUUUSEhVpxoooYwP8An9ad/coopokD/BThRRS7lBR/c/z2FFFNdRIRf8/pUg/z+dFFSMKKKKyA/9k=\n",
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=259x194>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "FXDiszJy4dFN",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1728974989047,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "FXDiszJy4dFN"
   },
   "outputs": [],
   "source": [
    "x_test = transforms(img).unsqueeze(0).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5amaEn8b4qse",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1728974990128,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "5amaEn8b4qse"
   },
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    logits = vgg(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "LJbrwbQT5IZD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 714,
     "status": "ok",
     "timestamp": 1728974990840,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "LJbrwbQT5IZD",
    "outputId": "56c683f5-ddc0-47e6-e8f0-65a87011fd3e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0487,  0.0487]], device='cuda:0')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "I6vFTODs5I8a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 468,
     "status": "ok",
     "timestamp": 1728975014160,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "I6vFTODs5I8a",
    "outputId": "755c5234-cc81-40be-bf25-f17662bc6a0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.4757, 0.5243]], device='cuda:0')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = torch.softmax(logits, dim=-1)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "aTmW7FVW8KpI",
   "metadata": {
    "executionInfo": {
     "elapsed": 524,
     "status": "ok",
     "timestamp": 1728975060121,
     "user": {
      "displayName": "Sriram Appusamy",
      "userId": "14728332375139881139"
     },
     "user_tz": -330
    },
    "id": "aTmW7FVW8KpI"
   },
   "outputs": [],
   "source": [
    "labels = scores.argmax(-1)"
   ]
  },
  {
   "cell_type": "code",
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