{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Connect with G Drive to import data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGScxOJ70QOb",
        "outputId": "b3fdf6a0-75f4-48f5-a918-26ffd25d51cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Organising the data in train & val folders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KvAYyxvr5w8_"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "\n",
        "base_dir = \"/content/drive/MyDrive/dataset\"\n",
        "train_dir = os.path.join(base_dir, \"train\")\n",
        "val_dir   = os.path.join(base_dir, \"val\")\n",
        "\n",
        "split_ratio = 0.8\n",
        "random.seed(42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "O7JMz2HN5zyz"
      },
      "outputs": [],
      "source": [
        "os.makedirs(train_dir, exist_ok=True)\n",
        "os.makedirs(val_dir, exist_ok=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Nxk2ZgP0TL6",
        "outputId": "736d210e-6d8f-44db-d4e0-d8504ddd4f3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✅ Dataset split completed safely.\n"
          ]
        }
      ],
      "source": [
        "classes = [\n",
        "    d for d in os.listdir(base_dir)\n",
        "    if os.path.isdir(os.path.join(base_dir, d))\n",
        "    and d not in [\"train\", \"val\"]\n",
        "]\n",
        "\n",
        "for cls in classes:\n",
        "    cls_path = os.path.join(base_dir, cls)\n",
        "    images = [f for f in os.listdir(cls_path) if os.path.isfile(os.path.join(cls_path, f))]\n",
        "\n",
        "    random.shuffle(images)\n",
        "    split_idx = int(len(images) * split_ratio)\n",
        "\n",
        "    train_images = images[:split_idx]\n",
        "    val_images   = images[split_idx:]\n",
        "\n",
        "    os.makedirs(os.path.join(train_dir, cls), exist_ok=True)\n",
        "    os.makedirs(os.path.join(val_dir, cls), exist_ok=True)\n",
        "\n",
        "    for img in train_images:\n",
        "        src = os.path.join(cls_path, img)\n",
        "        dst = os.path.join(train_dir, cls, img)\n",
        "        if not os.path.exists(dst):\n",
        "            shutil.copy2(src, dst)\n",
        "\n",
        "    for img in val_images:\n",
        "        src = os.path.join(cls_path, img)\n",
        "        dst = os.path.join(val_dir, cls, img)\n",
        "        if not os.path.exists(dst):\n",
        "            shutil.copy2(src, dst)\n",
        "\n",
        "print(\"✅ Dataset split completed safely.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "346oxppG0TIn"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torchvision import transforms\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "from torchvision import models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "iiVnJfVB0TF5"
      },
      "outputs": [],
      "source": [
        "train_path = \"/content/drive/MyDrive/dataset/train\"\n",
        "val_path   = \"/content/drive/MyDrive/dataset/val\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Transformations for train & val data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "A6H0FyKD0TDQ"
      },
      "outputs": [],
      "source": [
        "train_transforms = transforms.Compose([\n",
        "    transforms.Resize((128, 128)),\n",
        "    transforms.RandomHorizontalFlip(p=0.5),\n",
        "    transforms.RandomRotation(degrees=10),\n",
        "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                         [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "\n",
        "\n",
        "test_transforms = transforms.Compose([\n",
        "    transforms.Resize((128, 128)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                         [0.229, 0.224, 0.225])\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Creating custom dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F_MDI20Y0TAb"
      },
      "outputs": [],
      "source": [
        "class Car_Dataset(Dataset):\n",
        "  def __init__(self, dir_path, transform=None):\n",
        "    self.data = ImageFolder(dir_path, transform=transform)\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    return self.data[index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FALzW74L0S9h"
      },
      "outputs": [],
      "source": [
        "train_set = Car_Dataset(dir_path=train_path, transform=train_transforms)\n",
        "test_set  = Car_Dataset(dir_path=val_path, transform=test_transforms)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Distribution of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kuaznerr0S6i",
        "outputId": "e70ac4ff-9d3b-46a0-ec00-d7c976b8552b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Size of training dataset: 1840\n",
            "Size of testing dataset: 460\n"
          ]
        }
      ],
      "source": [
        "print(f\"Size of training dataset: {len(train_set)}\")\n",
        "print(f\"Size of testing dataset: {len(test_set)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "MFunN35S05gc",
        "outputId": "c477523d-babd-4a9e-e513-0d94b98bf421"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "labels = ['Training Data', 'Testing Data']\n",
        "sizes  = [len(train_set), len(test_set)]\n",
        "colors = ['lightgreen', 'red']\n",
        "explode = (0.1, 0)\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
        "        autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "plt.title('Training vs Testing Data Distribution')\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OooBwDDK05c-",
        "outputId": "e19d62f8-95ce-40b7-dc4d-b542f3822a91"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['F_Breakage', 'F_Crushed', 'F_Normal', 'R_Breakage', 'R_Crushed', 'R_Normal']"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_set.data.classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4q0i5-dR05aI",
        "outputId": "42db6c06-55dd-4e1c-90a5-a86072f80e61"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'F_Breakage': 0,\n",
              " 'F_Crushed': 1,\n",
              " 'F_Normal': 2,\n",
              " 'R_Breakage': 3,\n",
              " 'R_Crushed': 4,\n",
              " 'R_Normal': 5}"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_set.data.class_to_idx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uq8LqPt505XX",
        "outputId": "b492120d-2351-4db2-9a33-0b3d23c5c74b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[-2.1179, -2.1179, -2.1179,  ..., -2.1179, -2.1179, -2.1179],\n",
            "         [-2.1179, -2.1179, -2.1179,  ..., -2.1179, -2.1179, -2.1179],\n",
            "         [-2.1179, -2.1179, -2.1179,  ..., -2.1179, -2.1179, -2.1179],\n",
            "         ...,\n",
            "         [-2.1179, -2.1179, -2.1179,  ..., -2.1179, -2.1179, -2.1179],\n",
            "         [-2.1179, -2.1179, -2.1179,  ..., -2.1179, -2.1179, -2.1179],\n",
            "         [-2.1179, -2.1179, -2.1179,  ..., -2.1179, -2.1179, -2.1179]],\n",
            "\n",
            "        [[-2.0357, -2.0357, -2.0357,  ..., -2.0357, -2.0357, -2.0357],\n",
            "         [-2.0357, -2.0357, -2.0357,  ..., -2.0357, -2.0357, -2.0357],\n",
            "         [-2.0357, -2.0357, -2.0357,  ..., -2.0357, -2.0357, -2.0357],\n",
            "         ...,\n",
            "         [-2.0357, -2.0357, -2.0357,  ..., -2.0357, -2.0357, -2.0357],\n",
            "         [-2.0357, -2.0357, -2.0357,  ..., -2.0357, -2.0357, -2.0357],\n",
            "         [-2.0357, -2.0357, -2.0357,  ..., -2.0357, -2.0357, -2.0357]],\n",
            "\n",
            "        [[-1.8044, -1.8044, -1.8044,  ..., -1.8044, -1.8044, -1.8044],\n",
            "         [-1.8044, -1.8044, -1.8044,  ..., -1.8044, -1.8044, -1.8044],\n",
            "         [-1.8044, -1.8044, -1.8044,  ..., -1.8044, -1.8044, -1.8044],\n",
            "         ...,\n",
            "         [-1.8044, -1.8044, -1.8044,  ..., -1.8044, -1.8044, -1.8044],\n",
            "         [-1.8044, -1.8044, -1.8044,  ..., -1.8044, -1.8044, -1.8044],\n",
            "         [-1.8044, -1.8044, -1.8044,  ..., -1.8044, -1.8044, -1.8044]]]) 0\n"
          ]
        }
      ],
      "source": [
        "for image, label in train_set:\n",
        "  print(image, label)\n",
        "  break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Data loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "qUo1Bu4u1K9m"
      },
      "outputs": [],
      "source": [
        "train_loader = DataLoader(train_set, batch_size=64, shuffle=True, num_workers=2)\n",
        "eval_loader = DataLoader(test_set, batch_size=64, num_workers=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Starting with a custom cnn model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oiOpZxok1K6R"
      },
      "outputs": [],
      "source": [
        "class Car_Classifier_CNN(nn.Module):\n",
        "    def __init__(self, num_classes):\n",
        "        super().__init__()\n",
        "\n",
        "        self.features = nn.Sequential(\n",
        "            nn.Conv2d(3, 32, 3, padding=1),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2),\n",
        "\n",
        "            nn.Conv2d(32, 64, 3, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2),\n",
        "\n",
        "            nn.Conv2d(64, 128, 3, padding=1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2),\n",
        "\n",
        "            nn.Conv2d(128, 256, 3, padding=1),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2),\n",
        "        )\n",
        "\n",
        "        self.pool = nn.AdaptiveAvgPool2d((1,1))\n",
        "\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Linear(256, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.4),\n",
        "            nn.Linear(256, num_classes)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.features(x)\n",
        "        x = self.pool(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.classifier(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Setting up the device to free google colab gpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgNuIMyy1K3l",
        "outputId": "363ad408-1554-429e-baaf-739dbff2b8e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xmozs1eb1eEQ"
      },
      "outputs": [],
      "source": [
        "\n",
        "custom_cnn = Car_Classifier_CNN(num_classes=6).to(device)\n",
        "criterion = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
        "optimizer = torch.optim.Adam(custom_cnn.parameters(), lr=0.001, weight_decay=1e-4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdZYKezx1fuQ",
        "outputId": "51a68d56-f7bc-41ce-852f-0ba57d828c19"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [03:50<00:00,  7.94s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 1, Training Loss : 1.6773786544799805,  Accuracy : 0.32%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 2, Training Loss : 1.522877676733609,  Accuracy : 0.42%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 3, Training Loss : 1.39374067865569,  Accuracy : 0.47%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:32<00:00,  1.13s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 4, Training Loss : 1.2739563687094326,  Accuracy : 0.52%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 5, Training Loss : 1.2465400243627613,  Accuracy : 0.52%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 6, Training Loss : 1.2165370234127701,  Accuracy : 0.55%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:27<00:00,  1.05it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 7, Training Loss : 1.2077229721792813,  Accuracy : 0.56%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.00it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 8, Training Loss : 1.1977782085024078,  Accuracy : 0.55%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 9, Training Loss : 1.184841669839004,  Accuracy : 0.56%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 10, Training Loss : 1.1678476374724815,  Accuracy : 0.58%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 11, Training Loss : 1.156721328866893,  Accuracy : 0.58%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 12, Training Loss : 1.146267582630289,  Accuracy : 0.59%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 13, Training Loss : 1.1427013257454182,  Accuracy : 0.59%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 14, Training Loss : 1.1299961147637203,  Accuracy : 0.59%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 15, Training Loss : 1.1179325087317105,  Accuracy : 0.59%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 16, Training Loss : 1.114530057742678,  Accuracy : 0.61%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 17, Training Loss : 1.1143714210082745,  Accuracy : 0.61%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 18, Training Loss : 1.1033041579969998,  Accuracy : 0.62%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 19, Training Loss : 1.0992365705555882,  Accuracy : 0.62%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 20, Training Loss : 1.0829598657016097,  Accuracy : 0.63%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 21, Training Loss : 1.112258689156894,  Accuracy : 0.61%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 22, Training Loss : 1.0960757033578281,  Accuracy : 0.62%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 23, Training Loss : 1.0821778198768353,  Accuracy : 0.62%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 24, Training Loss : 1.0711393829049736,  Accuracy : 0.64%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 25, Training Loss : 1.062860877349459,  Accuracy : 0.64%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:27<00:00,  1.04it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 26, Training Loss : 1.0556538207777615,  Accuracy : 0.65%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:27<00:00,  1.04it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 27, Training Loss : 1.0603940836314498,  Accuracy : 0.64%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 28, Training Loss : 1.0447164588961109,  Accuracy : 0.65%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:27<00:00,  1.04it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 29, Training Loss : 1.0437344230454544,  Accuracy : 0.65%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 30, Training Loss : 1.0371732567918712,  Accuracy : 0.67%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "epochs = 30\n",
        "train_losses = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  custom_cnn.train()\n",
        "  running_loss = 0\n",
        "  total        = 0\n",
        "  correct      = 0\n",
        "\n",
        "  for images, labels in tqdm(train_loader):\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    outputs = custom_cnn(images)\n",
        "    loss = criterion(outputs, labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    running_loss += loss.item()\n",
        "    total += len(labels)\n",
        "\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    correct += (preds == labels).sum().item()\n",
        "\n",
        "  accuracy = (correct / total)\n",
        "  train_loss = running_loss / len(train_loader)\n",
        "  train_losses.append(train_loss)\n",
        "  print(f\"Epoch : {epoch + 1}, Training Loss : {train_loss},  Accuracy : {accuracy:.2f}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flyZcn0L1fqo",
        "outputId": "1ef6ebc4-0ad1-4464-a7c7-a7b745cdd86c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [01:05<00:00,  8.18s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation Accuracy : 0.4826%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "custom_cnn.eval()\n",
        "total = 0\n",
        "correct = 0\n",
        "accuracies = []\n",
        "all_preds = []\n",
        "all_labels = []\n",
        "\n",
        "with torch.no_grad():\n",
        "  for images, labels in tqdm(eval_loader):\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = custom_cnn(images)\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "    total += len(labels)\n",
        "    correct += (preds == labels).sum().item()\n",
        "    accuracies.append(correct/total)\n",
        "\n",
        "    all_preds.extend(preds.cpu().numpy())\n",
        "    all_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "val_accuracy = (correct / total)\n",
        "print(f\"Evaluation Accuracy : {val_accuracy:.4f}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Plotting the progress"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "7Dabx_JI1fnu",
        "outputId": "493673ae-2d6a-4d75-e4b8-202135784aca"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Plot Loss\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(train_losses, label='Training Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss Over Epochs')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "# Plot Accuracy\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(accuracies, label='Test Accuracy', color='green')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Test Accuracy Over Epochs')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Evaluatig the model performence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFz1dYhQ1pky",
        "outputId": "1e93f9fe-6a92-4f13-b915-3de63be81e1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "  F_Breakage       0.39      0.97      0.56       100\n",
            "   F_Crushed       0.50      0.01      0.02        80\n",
            "    F_Normal       0.82      0.53      0.64       100\n",
            "  R_Breakage       0.40      0.70      0.51        60\n",
            "   R_Crushed       0.50      0.07      0.12        60\n",
            "    R_Normal       0.83      0.42      0.56        60\n",
            "\n",
            "    accuracy                           0.48       460\n",
            "   macro avg       0.57      0.45      0.40       460\n",
            "weighted avg       0.57      0.48      0.42       460\n",
            "\n"
          ]
        }
      ],
      "source": [
        "class_names = eval_loader.dataset.data.classes\n",
        "print(classification_report(all_labels, all_preds, target_names=class_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Plotting the Confussion Metrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "pn5wPyMP1phc",
        "outputId": "d0c24628-a35a-42a2-f99b-8d4d8df215b9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(all_labels, all_preds)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"True\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Transfer Learning with Resnet 18"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9xJ6STrd7PRO"
      },
      "outputs": [],
      "source": [
        "\n",
        "class Car_Classifier_Resnet(nn.Module):\n",
        "    def __init__(self, num_classes):\n",
        "        super().__init__()\n",
        "\n",
        "        self.model = models.resnet18(weights=\"DEFAULT\")\n",
        "\n",
        "        for param in self.model.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "        for param in self.model.layer4.parameters():\n",
        "            param.requires_grad = True\n",
        "\n",
        "        for module in self.model.modules():\n",
        "            if isinstance(module, nn.BatchNorm2d):\n",
        "                for param in module.parameters():\n",
        "                    param.requires_grad = True\n",
        "\n",
        "        self.model.fc = nn.Sequential(\n",
        "            nn.Dropout(0.4),\n",
        "            nn.Linear(self.model.fc.in_features, num_classes)\n",
        "        )\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDCW6gRx7POB",
        "outputId": "f2eb9d59-8a3a-4f7e-bd75-dcd663eeca07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 106MB/s]\n"
          ]
        }
      ],
      "source": [
        "resnet_18 = Car_Classifier_Resnet(num_classes=6).to(device)\n",
        "criterion = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
        "optimizer = torch.optim.AdamW(resnet_18.parameters(), lr=0.002, weight_decay=1e-4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Hiuxq2k7TCD",
        "outputId": "54ba5c80-a5cc-46cf-af89-a1286478fb56"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 1, Training Loss : 0.5576681299456234,  Accuracy : 0.95%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.00it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 2, Training Loss : 0.5408628634337721,  Accuracy : 0.96%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 3, Training Loss : 0.5170059234931551,  Accuracy : 0.97%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 4, Training Loss : 0.5159030001738976,  Accuracy : 0.97%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 5, Training Loss : 0.5073884818060644,  Accuracy : 0.97%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 6, Training Loss : 0.48816245794296265,  Accuracy : 0.98%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 7, Training Loss : 0.49175768165752803,  Accuracy : 0.98%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 8, Training Loss : 0.489152239314441,  Accuracy : 0.98%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 9, Training Loss : 0.4910891816533845,  Accuracy : 0.98%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 10, Training Loss : 0.48319293301680993,  Accuracy : 0.98%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 11, Training Loss : 0.46835673163677083,  Accuracy : 0.99%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 12, Training Loss : 0.4842422676497492,  Accuracy : 0.98%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 13, Training Loss : 0.4725079947504504,  Accuracy : 0.99%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 14, Training Loss : 0.47634168842743185,  Accuracy : 0.98%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:28<00:00,  1.02it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch : 15, Training Loss : 0.4648646794516465,  Accuracy : 0.99%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "epochs = 15\n",
        "train_losses = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  resnet_18.train()\n",
        "  running_loss = 0\n",
        "  total        = 0\n",
        "  correct      = 0\n",
        "\n",
        "  for images, labels in tqdm(train_loader):\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    outputs = resnet_18(images)\n",
        "    loss = criterion(outputs, labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    running_loss += loss.item()\n",
        "    total += len(labels)\n",
        "\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    correct += (preds == labels).sum().item()\n",
        "\n",
        "  accuracy = (correct / total)\n",
        "  train_loss = running_loss / len(train_loader)\n",
        "  train_losses.append(train_loss)\n",
        "  print(f\"Epoch : {epoch + 1}, Training Loss : {train_loss},  Accuracy : {accuracy:.2f}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxACaLTf7S-z",
        "outputId": "33799345-4f3a-4f92-d138-cb0db3248788"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:06<00:00,  1.29it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation Accuracy : 0.7391%\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "resnet_18.eval()\n",
        "total = 0\n",
        "correct = 0\n",
        "accuracies = []\n",
        "all_preds = []\n",
        "all_labels = []\n",
        "\n",
        "with torch.no_grad():\n",
        "  for images, labels in tqdm(eval_loader):\n",
        "    images = images.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = resnet_18(images)\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "    total += len(labels)\n",
        "    correct += (preds == labels).sum().item()\n",
        "    accuracies.append(correct/total)\n",
        "\n",
        "    all_preds.extend(preds.cpu().numpy())\n",
        "    all_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "val_accuracy = (correct / total)\n",
        "print(f\"Evaluation Accuracy : {val_accuracy:.4f}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Plotting the progress"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "TpJSxAEf7qgZ",
        "outputId": "bc5a380f-1b8b-4850-c38d-eb30f036ef75"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Plot Loss\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(train_losses, label='Training Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss Over Epochs')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "# Plot Accuracy\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(accuracies, label='Test Accuracy', color='green')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Test Accuracy Over Epochs')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Classification Report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JCiN65r7qWg",
        "outputId": "cfbb225e-5d00-4e91-f140-6cd3cd66dc27"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "  F_Breakage       0.78      0.80      0.79       100\n",
            "   F_Crushed       0.65      0.65      0.65        80\n",
            "    F_Normal       0.82      0.81      0.81       100\n",
            "  R_Breakage       0.89      0.65      0.75        60\n",
            "   R_Crushed       0.59      0.75      0.66        60\n",
            "    R_Normal       0.74      0.72      0.73        60\n",
            "\n",
            "    accuracy                           0.74       460\n",
            "   macro avg       0.74      0.73      0.73       460\n",
            "weighted avg       0.75      0.74      0.74       460\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "class_names = eval_loader.dataset.data.classes\n",
        "print(classification_report(all_labels, all_preds, target_names=class_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Confussion Metrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "h7ft1esy7qTL",
        "outputId": "b1ad32df-e4b6-4117-dfcc-3c8dc7a11643"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "cm = confusion_matrix(all_labels, all_preds)\n",
        "\n",
        "plt.figure(figsize=(6, 5))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"True\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Saving the resnet model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "OugZc2ga7wdG"
      },
      "outputs": [],
      "source": [
        "\n",
        "torch.save(resnet_18.state_dict(), \"Damage_Classifier_Resnet_18.pth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Conclusion\n",
        "\n",
        "1. The custom model was seems to be under fitting underfitting\n",
        "\n",
        "2. The resnet model was giving slightly better result's even though i did not get very good eval score but it's still okay"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}