{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0944e75f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0944e75f",
    "outputId": "187519ec-ae03-47fa-d880-caeeb212f74f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n",
      "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (2.0.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.1)\n",
      "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.3)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.3)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a03d6f",
   "metadata": {
    "id": "67a03d6f"
   },
   "source": [
    "# Connecting to google drive to load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec16287c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ec16287c",
    "outputId": "a4f0c5b3-77c0-4682-d643-d689d95ea0db"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "867ccbfb",
   "metadata": {
    "id": "867ccbfb"
   },
   "source": [
    "# Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74b89f59",
   "metadata": {
    "id": "74b89f59"
   },
   "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 torchvision import models\n",
    "from tqdm import tqdm\n",
    "from torchvision import models\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report, confusion_matrix\n",
    "import os\n",
    "from transformers import get_cosine_schedule_with_warmup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfa6391",
   "metadata": {
    "id": "2bfa6391"
   },
   "source": [
    "# Custom dataloader class and transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6974fc41",
   "metadata": {
    "id": "6974fc41"
   },
   "outputs": [],
   "source": [
    "\n",
    "train_transforms = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\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((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                         [0.229, 0.224, 0.225])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ce8fccf",
   "metadata": {
    "id": "2ce8fccf"
   },
   "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": 6,
   "id": "8eff3c96",
   "metadata": {
    "id": "8eff3c96"
   },
   "outputs": [],
   "source": [
    "train_path = \"/content/drive/MyDrive/dataset/train\"\n",
    "test_path  = \"/content/drive/MyDrive/dataset/val\"\n",
    "\n",
    "\n",
    "train_set = Car_Dataset(dir_path=train_path, transform=train_transforms)\n",
    "test_set  = Car_Dataset(dir_path=test_path, transform=test_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "568fd4ca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "568fd4ca",
    "outputId": "aa16a7ec-2463-4c55-e8d9-e066365219a0"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{0: 'F_Breakage',\n",
       " 1: 'F_Crushed',\n",
       " 2: 'F_Normal',\n",
       " 3: 'R_Breakage',\n",
       " 4: 'R_Crushed',\n",
       " 5: 'R_Normal'}"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "class_to_idx = train_set.data.class_to_idx\n",
    "idx_to_class = {v: k for k, v in class_to_idx.items()}\n",
    "idx_to_class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e77a332",
   "metadata": {
    "id": "3e77a332"
   },
   "source": [
    "# Visualising the train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "oAlhV5N90Eyz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "id": "oAlhV5N90Eyz",
    "outputId": "a291e36e-ef12-4f5d-ae3d-bcd8b74c862c"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "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": "markdown",
   "id": "9614f04a",
   "metadata": {
    "id": "9614f04a"
   },
   "source": [
    "# Ploting the image distribution across the classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "HDE-TtNq0I2P",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 536
    },
    "id": "HDE-TtNq0I2P",
    "outputId": "1b200487-80cc-4509-acd0-89c94feeb2cd"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "def plot_class_distribution(data_path):\n",
    "    classes = os.listdir(data_path)\n",
    "    counts = []\n",
    "\n",
    "    for cls in classes:\n",
    "        cls_path = os.path.join(data_path, cls)\n",
    "        if os.path.isdir(cls_path):\n",
    "            counts.append(len(os.listdir(cls_path)))\n",
    "\n",
    "    plt.figure(figsize=(12,5))\n",
    "    plt.bar(classes, counts)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title(\"Train Class Distribution\")\n",
    "    plt.show()\n",
    "\n",
    "plot_class_distribution(\"/content/drive/MyDrive/dataset/train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4fd41d48",
   "metadata": {
    "id": "4fd41d48"
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_set, batch_size=64, shuffle=True, num_workers=8, pin_memory=True, persistent_workers=True)\n",
    "\n",
    "eval_loader = DataLoader(test_set, batch_size=64, num_workers=8, pin_memory=True, persistent_workers=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b47a31",
   "metadata": {
    "id": "94b47a31"
   },
   "source": "# Initializing the model"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a6b9c1e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6a6b9c1e",
    "outputId": "ae5c14e2-f164-41fe-b111-0a1b12cd2708"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "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": 15,
   "id": "21c8a4ba",
   "metadata": {
    "id": "21c8a4ba"
   },
   "outputs": [],
   "source": [
    "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.layer3.parameters():\n",
    "            param.requires_grad = True\n",
    "\n",
    "        for param in self.model.layer4.parameters():\n",
    "            param.requires_grad = True\n",
    "\n",
    "        self.model.fc = nn.Sequential(\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(self.model.fc.in_features, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(256, num_classes)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa339df",
   "metadata": {
    "id": "7aa339df"
   },
   "source": [
    "# Again using different LR for classifier and unfreezed layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5fb1adb8",
   "metadata": {
    "id": "5fb1adb8"
   },
   "outputs": [],
   "source": [
    "model = Car_Classifier_Resnet(num_classes=len(class_to_idx)).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.AdamW([\n",
    "    {\"params\": model.model.layer3.parameters(), \"lr\": 1e-5},\n",
    "    {\"params\": model.model.layer4.parameters(), \"lr\": 1e-5},\n",
    "    {\"params\": model.model.fc.parameters(), \"lr\": 1e-4}\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GnN56qwmQ3Gs"
   },
   "source": "# Early Stopping",
   "id": "GnN56qwmQ3Gs"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "-L5Ilp8mQ3Gt"
   },
   "outputs": [],
   "source": [
    "class EarlyStopping:\n",
    "    def __init__(self, patience=7, min_delta=0.001):\n",
    "        self.patience   = patience\n",
    "        self.min_delta  = min_delta\n",
    "        self.counter    = 0\n",
    "        self.best_score = None\n",
    "        self.early_stop = False\n",
    "\n",
    "    def __call__(self, val_acc):\n",
    "        if self.best_score is None:\n",
    "            self.best_score = val_acc\n",
    "        elif val_acc < self.best_score + self.min_delta:\n",
    "            self.counter += 1\n",
    "            print(f\"  EarlyStopping: {self.counter}/{self.patience}\")\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "        else:\n",
    "            self.best_score = val_acc\n",
    "            self.counter    = 0"
   ],
   "id": "-L5Ilp8mQ3Gt"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eV_yGEGnQ3Gt"
   },
   "source": "# Training Function",
   "id": "eV_yGEGnQ3Gt"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "lQK_xk7HQ3Gt"
   },
   "outputs": [],
   "source": [
    "def train_model(\n",
    "    model,\n",
    "    epochs,\n",
    "    train_loader,\n",
    "    eval_loader,\n",
    "    optimizer,\n",
    "    criterion,\n",
    "    device,\n",
    "    patience=7,\n",
    "    checkpoint_model_name=\"model\"\n",
    "):\n",
    "\n",
    "    print(\"Starting training...\")\n",
    "    print(f\"Epochs: {epochs} | Device: {device}\")\n",
    "    print(\"-\" * 60)\n",
    "\n",
    "    # ------------------------- SCHEDULER -------------------------\n",
    "    num_training_steps = epochs * len(train_loader)\n",
    "    num_warmup_steps   = int(0.1 * num_training_steps)\n",
    "\n",
    "    scheduler = get_cosine_schedule_with_warmup(\n",
    "        optimizer,\n",
    "        num_warmup_steps=num_warmup_steps,\n",
    "        num_training_steps=num_training_steps\n",
    "    )\n",
    "\n",
    "    early_stopping = EarlyStopping(patience=patience)\n",
    "\n",
    "    train_losses, train_accs = [], []\n",
    "    val_losses,   val_accs   = [], []\n",
    "\n",
    "    best_acc = 0.0\n",
    "\n",
    "    # ------------------------- TRAIN LOOP -------------------------\n",
    "    for epoch in range(epochs):\n",
    "\n",
    "        # --------------------- TRAIN ---------------------\n",
    "        model.train()\n",
    "        running_loss, correct, total = 0, 0, 0\n",
    "\n",
    "        for images, labels in tqdm(train_loader, desc=f\"Epoch {epoch+1} Training\"):\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            optimizer.zero_grad(set_to_none=True)\n",
    "\n",
    "            logits = model(images)\n",
    "            loss   = criterion(logits, labels)\n",
    "            loss.backward()\n",
    "\n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "            preds   = torch.argmax(logits, dim=1)\n",
    "            correct += (preds == labels).sum().item()\n",
    "            total   += labels.size(0)\n",
    "\n",
    "        train_loss = running_loss / len(train_loader)\n",
    "        train_acc  = 100 * correct / total\n",
    "        train_losses.append(train_loss)\n",
    "        train_accs.append(train_acc)\n",
    "\n",
    "        # --------------------- VALIDATION ---------------------\n",
    "        model.eval()\n",
    "        val_running_loss, val_correct, val_total = 0, 0, 0\n",
    "        all_preds, all_labels = [], []\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for images, labels in tqdm(eval_loader, desc=f\"Epoch {epoch+1} Validation\"):\n",
    "                images = images.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                logits = model(images)\n",
    "                loss   = criterion(logits, labels)\n",
    "\n",
    "                val_running_loss += loss.item()\n",
    "                preds   = torch.argmax(logits, dim=1)\n",
    "                val_correct += (preds == labels).sum().item()\n",
    "                val_total   += labels.size(0)\n",
    "\n",
    "                all_preds.extend(preds.cpu().numpy())\n",
    "                all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "        val_loss = val_running_loss / len(eval_loader)\n",
    "        val_acc  = 100 * val_correct / val_total\n",
    "\n",
    "        val_losses.append(val_loss)\n",
    "        val_accs.append(val_acc)\n",
    "\n",
    "        print(\n",
    "            f\"Results | \"\n",
    "            f\"Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.2f}% || \"\n",
    "            f\"Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.2f}%\"\n",
    "        )\n",
    "        print(\"-\" * 60)\n",
    "\n",
    "        # --------------------- SAVE BEST MODEL (BY ACCURACY) ---------------------\n",
    "        if val_acc > best_acc:\n",
    "            best_acc = val_acc\n",
    "            os.makedirs(\"checkpoints\", exist_ok=True)\n",
    "            torch.save({\n",
    "                \"model_state_dict\":     model.state_dict(),\n",
    "                \"optimizer_state_dict\": optimizer.state_dict(),\n",
    "                \"epoch\":                epoch,\n",
    "                \"val_acc\":              val_acc\n",
    "            }, f\"checkpoints/{checkpoint_model_name}.pt\")\n",
    "            print(f\"  Best model saved (Acc: {val_acc:.2f}%)\")\n",
    "\n",
    "        # --------------------- EARLY STOPPING ---------------------\n",
    "        early_stopping(val_acc)\n",
    "        if early_stopping.early_stop:\n",
    "            print(f\"Early stopping at epoch {epoch+1}\")\n",
    "            break\n",
    "\n",
    "    return train_losses, train_accs, val_losses, val_accs, all_preds, all_labels"
   ],
   "id": "lQK_xk7HQ3Gt"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mrkIxUZgQ3Gt"
   },
   "source": [
    "# Train the Model"
   ],
   "id": "mrkIxUZgQ3Gt"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xemDy_QEQ3Gt",
    "outputId": "f1780aa9-3973-4d16-84a4-ef51a16db740"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Starting training...\n",
      "Epochs: 100 | Device: cuda\n",
      "------------------------------------------------------------\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\rEpoch 1 Training:   0%|          | 0/29 [00:00<?, ?it/s]/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:432: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  self.check_worker_number_rationality()\n",
      "Epoch 1 Training: 100%|██████████| 29/29 [01:34<00:00,  3.24s/it]\n",
      "Epoch 1 Validation: 100%|██████████| 8/8 [00:26<00:00,  3.36s/it]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.8359 | Train Acc: 17.34% || Val Loss: 1.7960 | Val Acc: 22.39%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 22.39%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 2 Training: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it]\n",
      "Epoch 2 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.36it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.8131 | Train Acc: 20.05% || Val Loss: 1.7694 | Val Acc: 28.70%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 28.70%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 3 Training: 100%|██████████| 29/29 [00:32<00:00,  1.13s/it]\n",
      "Epoch 3 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.14it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.7685 | Train Acc: 23.59% || Val Loss: 1.7359 | Val Acc: 31.30%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 31.30%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 4 Training: 100%|██████████| 29/29 [00:30<00:00,  1.05s/it]\n",
      "Epoch 4 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.37it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.7045 | Train Acc: 30.33% || Val Loss: 1.6835 | Val Acc: 33.48%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 33.48%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 5 Training: 100%|██████████| 29/29 [00:32<00:00,  1.11s/it]\n",
      "Epoch 5 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.05it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.6434 | Train Acc: 34.18% || Val Loss: 1.6088 | Val Acc: 34.57%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 34.57%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 6 Training: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it]\n",
      "Epoch 6 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.43it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.5584 | Train Acc: 37.93% || Val Loss: 1.5080 | Val Acc: 37.39%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 37.39%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 7 Training: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it]\n",
      "Epoch 7 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.37it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.4352 | Train Acc: 45.38% || Val Loss: 1.3829 | Val Acc: 43.48%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 43.48%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 8 Training: 100%|██████████| 29/29 [00:29<00:00,  1.00s/it]\n",
      "Epoch 8 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.03it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.2920 | Train Acc: 49.40% || Val Loss: 1.2338 | Val Acc: 52.17%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 52.17%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 9 Training: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it]\n",
      "Epoch 9 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.38it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.1489 | Train Acc: 55.54% || Val Loss: 1.0641 | Val Acc: 59.57%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 59.57%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 10 Training: 100%|██████████| 29/29 [00:30<00:00,  1.04s/it]\n",
      "Epoch 10 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.31it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 1.0071 | Train Acc: 59.89% || Val Loss: 0.9361 | Val Acc: 62.61%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 62.61%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 11 Training: 100%|██████████| 29/29 [00:28<00:00,  1.00it/s]\n",
      "Epoch 11 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.05it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.8900 | Train Acc: 64.08% || Val Loss: 0.8604 | Val Acc: 65.00%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 65.00%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 12 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 12 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.45it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.8029 | Train Acc: 67.01% || Val Loss: 0.8249 | Val Acc: 64.78%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 13 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 13 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.40it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.7419 | Train Acc: 69.29% || Val Loss: 0.7945 | Val Acc: 66.52%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 66.52%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 14 Training: 100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n",
      "Epoch 14 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.03it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.6978 | Train Acc: 70.54% || Val Loss: 0.7597 | Val Acc: 66.30%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 15 Training: 100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n",
      "Epoch 15 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.30it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.6552 | Train Acc: 72.28% || Val Loss: 0.7413 | Val Acc: 68.04%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 68.04%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 16 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 16 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.48it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.6225 | Train Acc: 73.75% || Val Loss: 0.7227 | Val Acc: 69.13%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 69.13%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 17 Training: 100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n",
      "Epoch 17 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.22it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.5940 | Train Acc: 75.00% || Val Loss: 0.7131 | Val Acc: 67.83%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 18 Training: 100%|██████████| 29/29 [00:27<00:00,  1.04it/s]\n",
      "Epoch 18 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.06it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.5637 | Train Acc: 76.90% || Val Loss: 0.7207 | Val Acc: 70.00%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 70.00%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 19 Training: 100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n",
      "Epoch 19 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.43it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.5407 | Train Acc: 77.72% || Val Loss: 0.6890 | Val Acc: 71.09%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 71.09%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 20 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 20 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.44it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.5375 | Train Acc: 77.34% || Val Loss: 0.6909 | Val Acc: 70.00%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 21 Training: 100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n",
      "Epoch 21 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.23it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.5164 | Train Acc: 79.24% || Val Loss: 0.6734 | Val Acc: 72.17%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 72.17%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 22 Training: 100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n",
      "Epoch 22 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.09it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.4637 | Train Acc: 81.36% || Val Loss: 0.6726 | Val Acc: 70.87%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 23 Training: 100%|██████████| 29/29 [00:28<00:00,  1.00it/s]\n",
      "Epoch 23 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.45it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.4631 | Train Acc: 81.68% || Val Loss: 0.6571 | Val Acc: 72.39%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 72.39%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 24 Training: 100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n",
      "Epoch 24 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.47it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.4487 | Train Acc: 81.09% || Val Loss: 0.6693 | Val Acc: 72.17%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 25 Training: 100%|██████████| 29/29 [00:29<00:00,  1.00s/it]\n",
      "Epoch 25 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.07it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.4577 | Train Acc: 81.68% || Val Loss: 0.6525 | Val Acc: 73.48%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 73.48%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 26 Training: 100%|██████████| 29/29 [00:27<00:00,  1.05it/s]\n",
      "Epoch 26 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.23it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.4109 | Train Acc: 84.13% || Val Loss: 0.6680 | Val Acc: 73.48%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 27 Training: 100%|██████████| 29/29 [00:28<00:00,  1.00it/s]\n",
      "Epoch 27 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.48it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.3940 | Train Acc: 84.02% || Val Loss: 0.6574 | Val Acc: 73.70%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 73.70%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 28 Training: 100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n",
      "Epoch 28 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.46it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.3760 | Train Acc: 85.27% || Val Loss: 0.6619 | Val Acc: 73.91%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 73.91%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 29 Training: 100%|██████████| 29/29 [00:29<00:00,  1.00s/it]\n",
      "Epoch 29 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.06it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.3587 | Train Acc: 85.65% || Val Loss: 0.6462 | Val Acc: 75.22%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 75.22%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 30 Training: 100%|██████████| 29/29 [00:27<00:00,  1.04it/s]\n",
      "Epoch 30 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.31it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.3547 | Train Acc: 86.52% || Val Loss: 0.6510 | Val Acc: 74.57%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 31 Training: 100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n",
      "Epoch 31 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.45it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.3419 | Train Acc: 86.36% || Val Loss: 0.6465 | Val Acc: 74.57%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 2/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 32 Training: 100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n",
      "Epoch 32 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.32it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.3215 | Train Acc: 86.47% || Val Loss: 0.6794 | Val Acc: 73.91%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 3/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 33 Training: 100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n",
      "Epoch 33 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.04it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.3065 | Train Acc: 88.42% || Val Loss: 0.6688 | Val Acc: 74.57%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 4/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 34 Training: 100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n",
      "Epoch 34 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.41it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2855 | Train Acc: 89.35% || Val Loss: 0.6574 | Val Acc: 75.00%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 5/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 35 Training: 100%|██████████| 29/29 [00:29<00:00,  1.00s/it]\n",
      "Epoch 35 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.44it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2854 | Train Acc: 89.29% || Val Loss: 0.6678 | Val Acc: 75.65%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 75.65%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 36 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 36 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.15it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2782 | Train Acc: 90.16% || Val Loss: 0.6670 | Val Acc: 75.22%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 37 Training: 100%|██████████| 29/29 [00:27<00:00,  1.06it/s]\n",
      "Epoch 37 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.06it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2692 | Train Acc: 89.89% || Val Loss: 0.6670 | Val Acc: 75.87%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 75.87%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 38 Training: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it]\n",
      "Epoch 38 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.43it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2346 | Train Acc: 90.92% || Val Loss: 0.6792 | Val Acc: 75.00%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 39 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 39 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.47it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2441 | Train Acc: 91.41% || Val Loss: 0.6930 | Val Acc: 74.78%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 2/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 40 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 40 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.01it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2464 | Train Acc: 90.92% || Val Loss: 0.6975 | Val Acc: 75.00%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 3/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 41 Training: 100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n",
      "Epoch 41 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.26it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2244 | Train Acc: 91.30% || Val Loss: 0.7187 | Val Acc: 77.39%\n",
      "------------------------------------------------------------\n",
      "  Best model saved (Acc: 77.39%)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 42 Training: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it]\n",
      "Epoch 42 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.45it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.1938 | Train Acc: 93.10% || Val Loss: 0.7120 | Val Acc: 76.09%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 1/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 43 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 43 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.24it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.2070 | Train Acc: 92.12% || Val Loss: 0.7185 | Val Acc: 74.35%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 2/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 44 Training: 100%|██████████| 29/29 [00:28<00:00,  1.02it/s]\n",
      "Epoch 44 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.05it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.1964 | Train Acc: 93.26% || Val Loss: 0.7165 | Val Acc: 75.00%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 3/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 45 Training: 100%|██████████| 29/29 [00:29<00:00,  1.01s/it]\n",
      "Epoch 45 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.46it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.1976 | Train Acc: 93.26% || Val Loss: 0.7150 | Val Acc: 75.22%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 4/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 46 Training: 100%|██████████| 29/29 [00:29<00:00,  1.03s/it]\n",
      "Epoch 46 Validation: 100%|██████████| 8/8 [00:05<00:00,  1.42it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.1638 | Train Acc: 94.24% || Val Loss: 0.7279 | Val Acc: 75.00%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 5/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 47 Training: 100%|██████████| 29/29 [00:29<00:00,  1.02s/it]\n",
      "Epoch 47 Validation: 100%|██████████| 8/8 [00:07<00:00,  1.05it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.1735 | Train Acc: 94.13% || Val Loss: 0.7278 | Val Acc: 75.43%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 6/7\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 48 Training: 100%|██████████| 29/29 [00:28<00:00,  1.01it/s]\n",
      "Epoch 48 Validation: 100%|██████████| 8/8 [00:06<00:00,  1.29it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Results | Train Loss: 0.1694 | Train Acc: 94.02% || Val Loss: 0.7269 | Val Acc: 75.65%\n",
      "------------------------------------------------------------\n",
      "  EarlyStopping: 7/7\n",
      "Early stopping at epoch 48\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_losses, train_accuracy, eval_losses, eval_accuracies, all_preds, all_labels = train_model(\n",
    "    model                 = model,\n",
    "    train_loader          = train_loader,\n",
    "    eval_loader           = eval_loader,\n",
    "    optimizer             = optimizer,\n",
    "    criterion             = criterion,\n",
    "    device                = device,\n",
    "    epochs                = 100,\n",
    "    patience              = 7,\n",
    "    checkpoint_model_name = 'best_resnet_model'\n",
    ")"
   ],
   "id": "xemDy_QEQ3Gt"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lw7i9mcqQ3Gt"
   },
   "source": [
    "# Plotting Loss and Accuracy Curves"
   ],
   "id": "lw7i9mcqQ3Gt"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "YCqEr0HTQ3Gt",
    "outputId": "45373df5-f962-42af-ce57-796ee6ad638e"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "def plot_training_curves(train_losses, eval_losses, train_acc, eval_acc):\n",
    "    epochs = range(1, len(train_losses) + 1)\n",
    "\n",
    "    plt.figure(figsize=(12, 5))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(epochs, train_losses, label=\"Train Loss\")\n",
    "    plt.plot(epochs, eval_losses,  label=\"Val Loss\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.title(\"Loss Curve\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(epochs, train_acc, label=\"Train Acc\")\n",
    "    plt.plot(epochs, eval_acc,  label=\"Val Acc\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Accuracy (%)\")\n",
    "    plt.title(\"Accuracy Curve\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_training_curves(train_losses, eval_losses, train_accuracy, eval_accuracies)"
   ],
   "id": "YCqEr0HTQ3Gt"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T0QT2SzkQ3Gu"
   },
   "source": [
    "# Evaluation — Load Best Model & Run Predictions"
   ],
   "id": "T0QT2SzkQ3Gu"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tj236qu5Q3Gu",
    "outputId": "e87a0e8d-c565-4671-fbfe-cf013e0127c8"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Best model loaded from checkpoints/best_resnet_model.pt\n",
      "  Saved at epoch 41 | Val Acc: 77.39%\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Making predictions on eval_loader: 100%|██████████| 8/8 [00:05<00:00,  1.46it/s]\n"
     ]
    }
   ],
   "source": [
    "loaded_model = Car_Classifier_Resnet(num_classes=len(class_to_idx)).to(device)\n",
    "\n",
    "checkpoint_path = \"checkpoints/best_resnet_model.pt\"\n",
    "checkpoint = torch.load(checkpoint_path)\n",
    "loaded_model.load_state_dict(checkpoint['model_state_dict'])\n",
    "\n",
    "print(f\"Best model loaded from {checkpoint_path}\")\n",
    "print(f\"  Saved at epoch {checkpoint['epoch'] + 1} | Val Acc: {checkpoint['val_acc']:.2f}%\")\n",
    "\n",
    "loaded_model.eval()\n",
    "\n",
    "all_labels_loaded_model = []\n",
    "all_preds_loaded_model  = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, labels in tqdm(eval_loader, desc=\"Making predictions on eval_loader\"):\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        logits = loaded_model(images)\n",
    "        preds  = torch.argmax(logits, dim=1)\n",
    "\n",
    "        all_labels_loaded_model.extend(labels.cpu().numpy())\n",
    "        all_preds_loaded_model.extend(preds.cpu().numpy())"
   ],
   "id": "Tj236qu5Q3Gu"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Ovu8kldQ3Gu"
   },
   "source": [
    "# Metrics — Accuracy, Precision, Recall, Confusion Matrix & Classification Report"
   ],
   "id": "6Ovu8kldQ3Gu"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 906
    },
    "id": "iiW7_CX9Q3Gu",
    "outputId": "6c7a2314-e85d-4359-9082-23c7c21ecc68"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "=== Results from LOADED best model ===\n",
      "============================================================\n",
      "  Overall Accuracy  : 77.39%\n",
      "  Weighted Precision: 78.19%\n",
      "  Weighted Recall   : 77.39%\n",
      "============================================================\n",
      "\n",
      "--- Classification Report ---\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "  F_Breakage       0.83      0.78      0.80       100\n",
      "   F_Crushed       0.63      0.75      0.69        80\n",
      "    F_Normal       0.89      0.85      0.87       100\n",
      "  R_Breakage       0.81      0.72      0.76        60\n",
      "   R_Crushed       0.67      0.72      0.69        60\n",
      "    R_Normal       0.81      0.78      0.80        60\n",
      "\n",
      "    accuracy                           0.77       460\n",
      "   macro avg       0.77      0.77      0.77       460\n",
      "weighted avg       0.78      0.77      0.78       460\n",
      "\n",
      "--- Confusion Matrix ---\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "def print_metrics(all_labels, all_preds):\n",
    "    class_names = train_set.data.classes\n",
    "\n",
    "    acc       = accuracy_score(all_labels, all_preds) * 100\n",
    "    precision = precision_score(all_labels, all_preds, average='weighted', zero_division=0) * 100\n",
    "    recall    = recall_score(all_labels, all_preds, average='weighted', zero_division=0) * 100\n",
    "\n",
    "    print(\"=\" * 60)\n",
    "    print(f\"  Overall Accuracy  : {acc:.2f}%\")\n",
    "    print(f\"  Weighted Precision: {precision:.2f}%\")\n",
    "    print(f\"  Weighted Recall   : {recall:.2f}%\")\n",
    "    print(\"=\" * 60)\n",
    "\n",
    "    print(\"\\n--- Classification Report ---\")\n",
    "    report = classification_report(all_labels, all_preds, target_names=class_names, zero_division=0)\n",
    "    print(report)\n",
    "\n",
    "    print(\"--- Confusion Matrix ---\")\n",
    "    cm = confusion_matrix(all_labels, all_preds)\n",
    "    plt.figure(figsize=(max(6, len(class_names)), max(5, len(class_names) - 1)))\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
    "                xticklabels=class_names, yticklabels=class_names)\n",
    "    plt.xlabel(\"Predicted\")\n",
    "    plt.ylabel(\"True\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "print(\"\\n=== Results from LOADED best model ===\")\n",
    "print_metrics(all_labels_loaded_model, all_preds_loaded_model)"
   ],
   "id": "iiW7_CX9Q3Gu"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Uploading the model to HF",
   "id": "d3727b14de5d57cd"
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "from huggingface_hub import HfApi, notebook_login\n",
    "import torch\n",
    "\n",
    "# 1. Install huggingface_hub (if not already installed)\n",
    "!pip install huggingface_hub -q\n",
    "\n",
    "# 2. Log in to Hugging Face Hub\n",
    "# You will be prompted to enter your Hugging Face token.\n",
    "# You can find your token at https://huggingface.co/settings/tokens\n",
    "notebook_login()\n",
    "\n",
    "# 3. Define the repository ID and model name\n",
    "hf_username = \"junaid17\"\n",
    "model_name = \"car-damage-classifier\"\n",
    "repo_id = f\"{hf_username}/{model_name}\"\n",
    "\n",
    "# 4. Save the model's state_dict\n",
    "# The `loaded_model` from the previous cells is the best performing model.\n",
    "model_save_path = f\"./{model_name}.pt\"\n",
    "torch.save(loaded_model.state_dict(), model_save_path)\n",
    "print(f\"Model saved to {model_save_path}\")\n",
    "\n",
    "# 5. Push the saved model to Hugging Face Hub\n",
    "api = HfApi()\n",
    "\n",
    "# Create the repository if it doesn't exist\n",
    "api.create_repo(repo_id=repo_id, exist_ok=True, private=False)\n",
    "\n",
    "# Upload the model file\n",
    "api.upload_file(\n",
    "    path_or_fileobj=model_save_path,\n",
    "    path_in_repo=f\"{model_name}.pt\",\n",
    "    repo_id=repo_id,\n",
    "    repo_type=\"model\",\n",
    ")\n",
    "\n",
    "print(f\"Model '{model_name}.pt' uploaded to https://huggingface.co/{repo_id}\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 148,
     "referenced_widgets": [
      "7d239882978f4324b4dccc9d704631cc",
      "6885354eaa8b4981a76a9f316defa6f9",
      "d58da6596ce641f4b1c5c733cee3dcdc",
      "55cbc6e463464a4bba9fede68c3eefbd",
      "f4c0878312aa43d88edc94d67aa46655",
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