diff --git "a/26\352\270\260_BASE_\353\260\251\355\225\2315\354\243\274\354\260\250_ViT_\352\263\274\354\240\234_yunseo.ipynb" "b/26\352\270\260_BASE_\353\260\251\355\225\2315\354\243\274\354\260\250_ViT_\352\263\274\354\240\234_yunseo.ipynb"
new file mode 100644
index 0000000..e7fe0cf
--- /dev/null
+++ "b/26\352\270\260_BASE_\353\260\251\355\225\2315\354\243\274\354\260\250_ViT_\352\263\274\354\240\234_yunseo.ipynb"
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+          "model_module_version": "1.5.0",
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+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1️⃣ ResNet\n",
+        "ViT 실습에 앞서 성능을 비교하기 위한 CNN-based model로 ResNet 실습을 갖고 왔습니다! 코드 설명도 달아놓았는데 공부하실 때 도움되었음 좋겠네요,,\n",
+        "실습하실 때 사본 만들어서 편집하시면 될 것 같습니다~!"
+      ],
+      "metadata": {
+        "id": "FY_JefnxJX6d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "ohS9IIBITYP5"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np                                                       # 데이터 전처리, 배열 연산, 통계 계산 등에 사용\n",
+        "import torch                                                             # Tensor 연산, GPU 사용, 자동 미분 등을 담당\n",
+        "import torch.nn as nn                                                    # Conv2d, Linear, ReLU 같은 모델 구성 요소 정의 시 사용\n",
+        "import torch.nn.functional as F                                          # activation, loss function 등을 함수 형태로 제공\n",
+        "import matplotlib.pyplot as plt                                          # 그래프 및 이미지 시각화를 위해 사용\n",
+        "import seaborn as sns                                                    # matplotlib 기반의 고급 시각화 라이브러리\n",
+        "from torchvision import datasets, transforms                             # MNIST, CIFAR 같은 표준 이미지 데이터셋 제공 & 이미지 resize, normalize, tensor 변환 등 전처리 파이프라인 구성\n",
+        "from sklearn.metrics import confusion_matrix, classification_report      # 혼동행렬과 같이 모델 성능을 정량적으로 평가할 때 사용"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# set device\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
+      ],
+      "metadata": {
+        "id": "G63uC0X2VOGD"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# hyperparameter\n",
+        "batch_size = 32\n",
+        "num_epoch = 10                                                         # 시간 관계상 5회로 설정 (나중에 혼자 실습할 때는 10 이상으로 설정해주세요~)"
+      ],
+      "metadata": {
+        "id": "C3GcH4lPotdL"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = datasets.CIFAR10(root=\"../data/CIFAR10\",\n",
+        "                               train=True,\n",
+        "                               download=True,\n",
+        "                               transform=transforms.Compose([\n",
+        "                                   transforms.RandomHorizontalFlip(),    # 증강은 하나만 했습니다 (잘 모르는 분들은 data augmentation 검색!!)\n",
+        "                                   transforms.ToTensor(),\n",
+        "                                   transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
+        "                               ]))\n",
+        "test_dataset = datasets.CIFAR10(root=\"../data/CIFAR10\",\n",
+        "                              train=False,\n",
+        "                               transform=transforms.Compose([            # 테스트용 데이터는 증강할 필요가 없겠죠?\n",
+        "                                   transforms.ToTensor(),\n",
+        "                                   transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
+        "                               ]))\n",
+        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
+        "                                           batch_size=batch_size,\n",
+        "                                           shuffle=True)                 # shuffle의 의미는 2주차 세션 실습때 다뤘습니다! 기억 안나면 복습!\n",
+        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
+        "                                           batch_size=batch_size,\n",
+        "                                           shuffle=False)"
+      ],
+      "metadata": {
+        "id": "tGgmA-4-o0Ly"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# DataLoader가 제대로 동작했는지 확인\n",
+        "for (X_train, y_train) in train_loader:\n",
+        "    print(f\"X_train: {X_train.size()}, type: {X_train.type()}\")\n",
+        "    print(f\"y_train: {y_train.size()}, type: {y_train.type()}\")\n",
+        "    break"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hFDuRRXgo8FK",
+        "outputId": "6dc562fd-890c-474e-ce9e-472c3f3bbd45"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X_train: torch.Size([32, 3, 32, 32]), type: torch.FloatTensor\n",
+            "y_train: torch.Size([32]), type: torch.LongTensor\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 그냥 데이터 체크해보기 (색이 바랜 건 정규화 때문!)\n",
+        "pltsize = 1\n",
+        "plt.figure(figsize=(10*pltsize, pltsize))\n",
+        "for i in range(10):\n",
+        "    plt.subplot(1, 10, i + 1)\n",
+        "    plt.axis('off')\n",
+        "    plt.imshow(np.transpose(X_train[i], (1, 2, 0)))\n",
+        "    plt.title(str(y_train[i].item()))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 311
+        },
+        "id": "BdZWr_f7o-JT",
+        "outputId": "f94d5191-40b4-4df9-d35e-62213cc105f8"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.81960785..1.0].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.94509804..0.94509804].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..0.90588236].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.99215686..0.8666667].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8666667..0.78039217].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.9529412..0.8980392].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8117647..1.0].\n",
+            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92941177..1.0].\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x100 with 10 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Residual Block (skip connection 만들기)\n",
+        "class BasicBlock(nn.Module):\n",
+        "    def __init__(self, in_planes, planes, stride = 1):\n",
+        "        super(BasicBlock, self).__init__()\n",
+        "\n",
+        "        # 첫 번째 3x3 convolution\n",
+        "        self.conv1 = nn.Conv2d(in_planes, planes,\n",
+        "            kernel_size=3,\n",
+        "            stride=stride,\n",
+        "            padding=1,\n",
+        "            bias=False\n",
+        "        )\n",
+        "        self.bn1 = nn.BatchNorm2d(planes)\n",
+        "\n",
+        "        # 두 번째 3x3 convolution\n",
+        "        self.conv2 = nn.Conv2d(planes, planes,\n",
+        "            kernel_size=3,\n",
+        "            stride=1,\n",
+        "            padding=1,\n",
+        "            bias=False\n",
+        "        )\n",
+        "        self.bn2 = nn.BatchNorm2d(planes)\n",
+        "\n",
+        "        # Skip connection\n",
+        "        self.shortcut = nn.Sequential()\n",
+        "\n",
+        "        # 입력과 출력 차원이 다를 경우 1x1 convolution으로 맞춤\n",
+        "        if stride != 1 or in_planes != planes:\n",
+        "            self.shortcut = nn.Sequential(\n",
+        "                nn.Conv2d(in_planes, planes,\n",
+        "                          kernel_size=1,\n",
+        "                          stride=stride,\n",
+        "                          bias=False),\n",
+        "                nn.BatchNorm2d(planes)\n",
+        "            )\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Conv → BN → ReLU\n",
+        "        out = F.relu(self.bn1(self.conv1(x)))\n",
+        "        # Conv → BN\n",
+        "        out = self.bn2(self.conv2(out))\n",
+        "        # skip connection\n",
+        "        out += self.shortcut(x)\n",
+        "        # 최종 ReLU\n",
+        "        out = F.relu(out)\n",
+        "        return out"
+      ],
+      "metadata": {
+        "id": "3LWXUI7IpFQN"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 전체 ResNet 모델 구조 정의\n",
+        "class ResNet(nn.Module):\n",
+        "    def __init__(self, block=BasicBlock, num_blocks=[2, 2, 2, 2], num_classes=10):\n",
+        "        super(ResNet, self).__init__()\n",
+        "\n",
+        "        # 현재 feature channel 수\n",
+        "        self.in_planes = 16\n",
+        "\n",
+        "        # 입력 이미지(3채널 RGB)를 16채널 feature map으로 변환\n",
+        "        self.conv1 = nn.Conv2d(3, 16,\n",
+        "            kernel_size=3,\n",
+        "            stride=1,\n",
+        "            padding=1,\n",
+        "            bias=False\n",
+        "        )\n",
+        "        self.bn1 = nn.BatchNorm2d(16)\n",
+        "\n",
+        "        # Residual block들을 단계별로 쌓음\n",
+        "        # 뒤로 갈수록 channel 증가 + spatial size 감소\n",
+        "        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)\n",
+        "        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)\n",
+        "        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)\n",
+        "        self.layer4 = self._make_layer(block, 128, num_blocks[3], stride=2)\n",
+        "\n",
+        "        # 과적합 방지를 위한 dropout\n",
+        "        self.dropout = nn.Dropout(p=0.5)\n",
+        "\n",
+        "        # 최종 fully connected layer\n",
+        "        self.linear = nn.Linear(128, num_classes)\n",
+        "\n",
+        "    # Residual block 여러 개를 하나의 layer로 만드는 함수\n",
+        "    def _make_layer(self, block, planes, num_blocks, stride):\n",
+        "        strides = [stride] + [1] * (num_blocks - 1)\n",
+        "        layers = []\n",
+        "        for stride in strides:\n",
+        "            layers.append(block(self.in_planes, planes, stride))\n",
+        "            self.in_planes = planes\n",
+        "        return nn.Sequential(*layers)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # 초기 convolution\n",
+        "        out = F.relu(self.bn1(self.conv1(x)))\n",
+        "        # Residual layers 통과\n",
+        "        out = self.layer1(out)\n",
+        "        out = self.layer2(out)\n",
+        "        out = self.layer3(out)\n",
+        "        out = self.layer4(out)\n",
+        "        # Global Average Pooling\n",
+        "        out = F.adaptive_avg_pool2d(out, (1,1))\n",
+        "        # Flatten\n",
+        "        out = out.view(out.size(0), -1)\n",
+        "        # 최종\n",
+        "        out = self.linear(out)\n",
+        "        return out"
+      ],
+      "metadata": {
+        "id": "NjLBz0vgpIl8"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# set model, optimizer, loss fn\n",
+        "model = ResNet().to(device)\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "\n",
+        "# 모델 구조 확인\n",
+        "display(model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "hRcM2vR5pLdf",
+        "outputId": "22fb0641-5836-42a1-ea9b-0faf25f246cc"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "ResNet(\n",
+              "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "  (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "  (layer1): Sequential(\n",
+              "    (0): BasicBlock(\n",
+              "      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential()\n",
+              "    )\n",
+              "    (1): BasicBlock(\n",
+              "      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential()\n",
+              "    )\n",
+              "  )\n",
+              "  (layer2): Sequential(\n",
+              "    (0): BasicBlock(\n",
+              "      (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential(\n",
+              "        (0): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (1): BasicBlock(\n",
+              "      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential()\n",
+              "    )\n",
+              "  )\n",
+              "  (layer3): Sequential(\n",
+              "    (0): BasicBlock(\n",
+              "      (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential(\n",
+              "        (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (1): BasicBlock(\n",
+              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential()\n",
+              "    )\n",
+              "  )\n",
+              "  (layer4): Sequential(\n",
+              "    (0): BasicBlock(\n",
+              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential(\n",
+              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (1): BasicBlock(\n",
+              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "      (shortcut): Sequential()\n",
+              "    )\n",
+              "  )\n",
+              "  (dropout): Dropout(p=0.5, inplace=False)\n",
+              "  (linear): Linear(in_features=128, out_features=10, bias=True)\n",
+              ")"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 모델 평가 (Validation / Test)\n",
+        "def evaluate(model, data_loader, criterion, device):\n",
+        "    model.eval()      # evaluation 모드 (dropout, batchnorm 비활성화)\n",
+        "    total_loss = 0\n",
+        "    correct = 0\n",
+        "    total = 0\n",
+        "\n",
+        "    # gradient 계산 비활성화 : 메모리 절약 + 속도 향상\n",
+        "    with torch.no_grad():\n",
+        "        for image, label in data_loader:\n",
+        "            image, label = image.to(device), label.to(device)\n",
+        "\n",
+        "            # forward pass\n",
+        "            output = model(image)\n",
+        "\n",
+        "            # batch loss 누적\n",
+        "            total_loss += criterion(output, label).item() * image.size(0)\n",
+        "\n",
+        "            # 가장 큰 logit을 예측 클래스로 선택\n",
+        "            prediction = output.argmax(dim=1)\n",
+        "\n",
+        "            # accuracy 계산\n",
+        "            correct += prediction.eq(label).sum().item()\n",
+        "            total += label.size(0)\n",
+        "\n",
+        "    avg_loss = total_loss / total\n",
+        "    accuracy = 100. * correct / total\n",
+        "    return avg_loss, accuracy\n",
+        "\n",
+        "# 학습 진행 상황 출력용 progress bar (25기대표는 progress bar or 타이머 없이는 못삽니다..)\n",
+        "def print_progress_bar(iteration, total, length=30):\n",
+        "    filled = int(length * iteration // total)\n",
+        "    bar = '█' * filled + '-' * (length - filled)\n",
+        "    print(f\"\\rProgress: |{bar}| {iteration}/{total} batches\", end='')\n",
+        "\n",
+        "def train(model, train_loader, optimizer, criterion, device):\n",
+        "    model.train()      # training 모드 (dropout, batchnorm 활성화)\n",
+        "    running_loss = 0\n",
+        "    correct = 0\n",
+        "    total = 0\n",
+        "    total_batches = len(train_loader)\n",
+        "\n",
+        "    for batch_idx, (image, label) in enumerate(train_loader):\n",
+        "        image, label = image.to(device), label.to(device)\n",
+        "\n",
+        "        # gradient 초기화\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # forward\n",
+        "        output = model(image)\n",
+        "\n",
+        "        # loss 계산\n",
+        "        loss = criterion(output, label)\n",
+        "\n",
+        "        # backward (gradient 계산)\n",
+        "        loss.backward()\n",
+        "\n",
+        "        # parameter 업데이트\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # loss 및 accuracy 누적\n",
+        "        running_loss += loss.item() * image.size(0)\n",
+        "        pred = output.argmax(dim=1)\n",
+        "        correct += pred.eq(label).sum().item()\n",
+        "        total += label.size(0)\n",
+        "\n",
+        "        # 진행 상황 출력\n",
+        "        print_progress_bar(batch_idx + 1, total_batches)\n",
+        "\n",
+        "    print()\n",
+        "\n",
+        "    avg_loss = running_loss / total\n",
+        "    accuracy = 100. * correct / total\n",
+        "    return avg_loss, accuracy"
+      ],
+      "metadata": {
+        "id": "Qsx6CTPqpN3v"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Early stopping을 위한 변수 초기화 (현재 epoch = 5 라서 의미는 전혀 없지만 나중에 높은 숫자로 설정했을 때 좋습니다!)\n",
+        "best_val_loss = float('inf')      # 현재까지의 가장 낮은 validation loss (초기값은 무한대)\n",
+        "patience = 5                      # val_loss가 개선되지 않아도 기다려줄 epoch 수\n",
+        "patience_counter = 0              # 개선 안 된 epoch가 몇 번 연속인지 카운트\n",
+        "\n",
+        "# # epoch별 loss/accuracy 기록용 리스트\n",
+        "train_losses, val_losses = [], []\n",
+        "train_accuracies, val_accuracies = [], []\n",
+        "\n",
+        "# 학습 루프 (epoch 반복)\n",
+        "for epoch in range(1, num_epoch + 1):\n",
+        "\n",
+        "    # 1) 한 epoch 학습\n",
+        "    train_loss, train_acc = train(model, train_loader, optimizer, criterion, device)\n",
+        "\n",
+        "    # 2) 평가\n",
+        "    val_loss, val_acc = evaluate(model, test_loader, criterion, device)\n",
+        "\n",
+        "    # 3) 기록 저장 (그래프 그릴 때 사용)\n",
+        "    train_losses.append(train_loss)\n",
+        "    val_losses.append(val_loss)\n",
+        "    train_accuracies.append(train_acc)\n",
+        "    val_accuracies.append(val_acc)\n",
+        "\n",
+        "    # 4) 진행 상황 출력\n",
+        "    print(f\"\\n[Epoch {epoch}]\")\n",
+        "    print(f\"Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.2f}%\")\n",
+        "    print(f\"Val Loss:   {val_loss:.4f}, Val Accuracy:   {val_acc:.2f}%\\n\")\n",
+        "\n",
+        "    # Early stopping : val_loss가 더 이상 좋아지지 않으면 학습 중단 (과적합 방지용인데, 현재는 무의미!)\n",
+        "    if val_loss < best_val_loss:\n",
+        "        # 성능이 개선되면 best 기록 갱신 + 현재 모델 저장\n",
+        "        best_val_loss = val_loss\n",
+        "        best_model_state = model.state_dict()\n",
+        "        patience_counter = 0\n",
+        "    else:\n",
+        "        # 성능이 개선되지 않으면 카운터 증가\n",
+        "        patience_counter += 1\n",
+        "        if patience_counter >= patience:\n",
+        "            print(f\"Early stopping triggered at epoch {epoch}\")\n",
+        "            break"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qOEZMgXzpQAY",
+        "outputId": "93eae489-854a-498f-ed82-ccf23f9984cf"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 1]\n",
+            "Train Loss: 1.2726, Train Accuracy: 53.98%\n",
+            "Val Loss:   0.9375, Val Accuracy:   67.11%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 2]\n",
+            "Train Loss: 0.8354, Train Accuracy: 70.76%\n",
+            "Val Loss:   0.7623, Val Accuracy:   74.31%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 3]\n",
+            "Train Loss: 0.6868, Train Accuracy: 76.14%\n",
+            "Val Loss:   0.6944, Val Accuracy:   76.36%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 4]\n",
+            "Train Loss: 0.5947, Train Accuracy: 79.37%\n",
+            "Val Loss:   0.6039, Val Accuracy:   79.45%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 5]\n",
+            "Train Loss: 0.5254, Train Accuracy: 81.76%\n",
+            "Val Loss:   0.5567, Val Accuracy:   80.81%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 6]\n",
+            "Train Loss: 0.4691, Train Accuracy: 83.80%\n",
+            "Val Loss:   0.5485, Val Accuracy:   81.26%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 7]\n",
+            "Train Loss: 0.4228, Train Accuracy: 85.36%\n",
+            "Val Loss:   0.5421, Val Accuracy:   81.64%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 8]\n",
+            "Train Loss: 0.3830, Train Accuracy: 86.57%\n",
+            "Val Loss:   0.5060, Val Accuracy:   82.97%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 9]\n",
+            "Train Loss: 0.3471, Train Accuracy: 88.08%\n",
+            "Val Loss:   0.5034, Val Accuracy:   82.81%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 10]\n",
+            "Train Loss: 0.3123, Train Accuracy: 89.02%\n",
+            "Val Loss:   0.5079, Val Accuracy:   83.15%\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# loss, acc curve (다음에 기회가 된다면 Wandb라는 것도 알려드릴게요!)\n",
+        "epochs = range(1, len(train_losses) + 1)\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, val_losses, label='Validation Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.title('Loss Curve')\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(epochs, train_accuracies, label='Train Acc')\n",
+        "plt.plot(epochs, val_accuracies, label='Validation Acc')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy (%)')\n",
+        "plt.legend()\n",
+        "plt.title('Accuracy Curve')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "c7Bv3nH9pZxg",
+        "outputId": "254f5420-2447-4286-b1ae-53cc664b30c2"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# confusion matrix\n",
+        "\n",
+        "# 1) 클래스 이름\n",
+        "class_names = [\n",
+        "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
+        "]\n",
+        "\n",
+        "# 2) 예측 결과 수집\n",
+        "all_preds = []\n",
+        "all_labels = []\n",
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "    for images, labels in test_loader:\n",
+        "        images, labels = images.to(device), labels.to(device)\n",
+        "        outputs = model(images)\n",
+        "        _, predicted = torch.max(outputs, 1)\n",
+        "        all_preds.extend(predicted.cpu().numpy())\n",
+        "        all_labels.extend(labels.cpu().numpy())\n",
+        "\n",
+        "# 3) confusion matrix 시각화\n",
+        "cm = confusion_matrix(all_labels, all_preds, normalize='true')\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues',\n",
+        "            xticklabels=class_names, yticklabels=class_names)\n",
+        "plt.xlabel('Predicted Label')\n",
+        "plt.ylabel('True Label')\n",
+        "plt.title('Confusion Matrix')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# 4) classification report 출력\n",
+        "report = classification_report(all_labels, all_preds, target_names=class_names)\n",
+        "print(\"Classification Report:\\n\", report)\n",
+        "\n",
+        "# cat vs dog가 눈에 띄네요 :) epoch을 늘리면 성능이 좋아질 거예요"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 941
+        },
+        "id": "mGOSBBqmqc_t",
+        "outputId": "0e3bd69f-c6c8-425c-87aa-eff5ac44f4bf"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "    airplane       0.86      0.84      0.85      1000\n",
+            "  automobile       0.92      0.92      0.92      1000\n",
+            "        bird       0.80      0.74      0.77      1000\n",
+            "         cat       0.63      0.71      0.67      1000\n",
+            "        deer       0.86      0.76      0.81      1000\n",
+            "         dog       0.70      0.81      0.75      1000\n",
+            "        frog       0.90      0.85      0.87      1000\n",
+            "       horse       0.88      0.86      0.87      1000\n",
+            "        ship       0.91      0.91      0.91      1000\n",
+            "       truck       0.91      0.91      0.91      1000\n",
+            "\n",
+            "    accuracy                           0.83     10000\n",
+            "   macro avg       0.84      0.83      0.83     10000\n",
+            "weighted avg       0.84      0.83      0.83     10000\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2️⃣Vision Transformer\n",
+        "메인 주제인 ViT를 다뤄보겠습니다. epoch을 높게 수정하여 직접 ResNet과 성능 비교하면 재미있을 것 같아요!"
+      ],
+      "metadata": {
+        "id": "7OqrQJBgJSXH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import timm                      # ResNet, ViT, EfficientNet 등 다양한 최신 pretrained 모델을 쉽게 불러올 수 있음\n",
+        "\n",
+        "# ResNet도 불러올 수 있습니다! (ResNet은 shortcut 구조를 직접 보는게 좋을 것 같아서 설계해봤습니다 ㅎㅎ!)\n",
+        "model = timm.create_model(\n",
+        "    \"vit_tiny_patch16_224\",      # 사용할 ViT 모델 구조 (실습 시간 줄이기 위해 tiny 버전, patch size 16, 기본 입력 224)\n",
+        "    pretrained=True,             # 사전학습된 가중치 사용 여부 (ViT를 직접 설계하지 않고 timm을 통해 불러오는 이유!)\n",
+        "    img_size=32,                 # 입력 이미지 크기 (CIFAR-10: 32x32이기 때문. Resize augmentation을 하셔도 됩니다!)\n",
+        "    patch_size=4,                # 이미지를 4x4 patch로 분할\n",
+        "    num_classes=10               # 최종 분류 클래스 수 (CIFAR-10)\n",
+        ")\n",
+        "model = model.to(device)         # 모델을 GPU 또는 CPU로 이동"
+      ],
+      "metadata": {
+        "id": "zWH9LLO932dF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160,
+          "referenced_widgets": [
+            "625e87e959f843e6aaa43ad4f665c30a",
+            "359b15f37405493684c2c536a49b4bea",
+            "66f9efd3139f423288440be36f9b37f6",
+            "e8661dd5f7554d50b58914af26d6abb3",
+            "6462502b3d4a4172a5e816ff9bb42e2f",
+            "89331893267c4ed192c4238a6090c162",
+            "42265e1cc94d427db84c172d6bf8bb66",
+            "df847077f3bb4e019a274fbf020d3ae6",
+            "243a3646f2f840ee9cc7b43159ff25d4",
+            "ea9333ed35b1466886148ee711ff092a",
+            "c875763f262944398b284be57c9b83a3"
+          ]
+        },
+        "outputId": "a10fa1d6-77f2-48de-cb1f-fe5eafc0cb2c"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/22.9M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "625e87e959f843e6aaa43ad4f665c30a"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "num_epoch = 10                  # epoch = 3 으로 줄여볼게요..! (나중에 혼자 실습할 때는 10 이상으로 설정해주세요~)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = torch.optim.AdamW(      # AdamW optimizer 사용 (Vision Transformer에 자주 사용됨)\n",
+        "    model.parameters(),             # 학습할 모든 파라미터\n",
+        "    lr=3e-4,                        # 3e-4 = 0.0003\n",
+        "    weight_decay=0.05               # weight decay (L2 regularization) → 과적합 방지\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "VzB178hiArW5"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 위 코드 복붙\n",
+        "best_val_loss = float('inf')\n",
+        "patience = 5\n",
+        "patience_counter = 0\n",
+        "\n",
+        "train_losses, val_losses = [], []\n",
+        "train_accuracies, val_accuracies = [], []\n",
+        "\n",
+        "for epoch in range(1, num_epoch + 1):\n",
+        "    train_loss, train_acc = train(model, train_loader, optimizer, criterion, device)\n",
+        "    val_loss, val_acc = evaluate(model, test_loader, criterion, device)\n",
+        "\n",
+        "    train_losses.append(train_loss)\n",
+        "    val_losses.append(val_loss)\n",
+        "    train_accuracies.append(train_acc)\n",
+        "    val_accuracies.append(val_acc)\n",
+        "\n",
+        "    print(f\"\\n[Epoch {epoch}]\")\n",
+        "    print(f\"Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.2f}%\")\n",
+        "    print(f\"Val Loss:   {val_loss:.4f}, Val Accuracy:   {val_acc:.2f}%\\n\")\n",
+        "\n",
+        "    # Early Stopping\n",
+        "    if val_loss < best_val_loss:\n",
+        "        best_val_loss = val_loss\n",
+        "        best_model_state = model.state_dict()\n",
+        "        patience_counter = 0\n",
+        "    else:\n",
+        "        patience_counter += 1\n",
+        "        if patience_counter >= patience:\n",
+        "            print(f\"Early stopping triggered at epoch {epoch}\")\n",
+        "            break"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xMUeeG9y4GA6",
+        "outputId": "e4193fe5-db04-4530-a8e6-1b2a743a186d"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 1]\n",
+            "Train Loss: 0.4166, Train Accuracy: 85.76%\n",
+            "Val Loss:   0.3280, Val Accuracy:   88.60%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 2]\n",
+            "Train Loss: 0.2773, Train Accuracy: 90.42%\n",
+            "Val Loss:   0.3063, Val Accuracy:   89.64%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 3]\n",
+            "Train Loss: 0.2353, Train Accuracy: 91.98%\n",
+            "Val Loss:   0.2781, Val Accuracy:   90.81%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 4]\n",
+            "Train Loss: 0.2074, Train Accuracy: 92.75%\n",
+            "Val Loss:   0.3038, Val Accuracy:   89.68%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 5]\n",
+            "Train Loss: 0.1769, Train Accuracy: 93.99%\n",
+            "Val Loss:   0.3276, Val Accuracy:   89.70%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 6]\n",
+            "Train Loss: 0.1577, Train Accuracy: 94.61%\n",
+            "Val Loss:   0.3076, Val Accuracy:   90.16%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 7]\n",
+            "Train Loss: 0.1489, Train Accuracy: 94.77%\n",
+            "Val Loss:   0.3415, Val Accuracy:   89.45%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 8]\n",
+            "Train Loss: 0.1378, Train Accuracy: 95.18%\n",
+            "Val Loss:   0.3010, Val Accuracy:   90.11%\n",
+            "\n",
+            "Early stopping triggered at epoch 8\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 위 코드 복붙 2\n",
+        "epochs = range(1, len(train_losses) + 1)\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, val_losses, label='Validation Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.title('Loss Curve')\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(epochs, train_accuracies, label='Train Acc')\n",
+        "plt.plot(epochs, val_accuracies, label='Validation Acc')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy (%)')\n",
+        "plt.legend()\n",
+        "plt.title('Accuracy Curve')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "bFpln93VCLtk",
+        "outputId": "b12f52e0-c0c0-4040-9f45-180b7007baf6"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 위 코드 복붙 3\n",
+        "all_preds = []\n",
+        "all_labels = []\n",
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "    for images, labels in test_loader:\n",
+        "        images, labels = images.to(device), labels.to(device)\n",
+        "        outputs = model(images)\n",
+        "        _, predicted = torch.max(outputs, 1)\n",
+        "        all_preds.extend(predicted.cpu().numpy())\n",
+        "        all_labels.extend(labels.cpu().numpy())\n",
+        "\n",
+        "cm = confusion_matrix(all_labels, all_preds, normalize='true')\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues',\n",
+        "            xticklabels=class_names, yticklabels=class_names)\n",
+        "plt.xlabel('Predicted Label')\n",
+        "plt.ylabel('True Label')\n",
+        "plt.title('Confusion Matrix')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "report = classification_report(all_labels, all_preds, target_names=class_names)\n",
+        "print(\"Classification Report:\\n\", report)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 941
+        },
+        "id": "xn8jsbKyCNQH",
+        "outputId": "6361122c-98bf-4a2f-fd33-8c766c018ac0"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "    airplane       0.92      0.92      0.92      1000\n",
+            "  automobile       0.96      0.93      0.95      1000\n",
+            "        bird       0.92      0.85      0.88      1000\n",
+            "         cat       0.81      0.79      0.80      1000\n",
+            "        deer       0.85      0.94      0.89      1000\n",
+            "         dog       0.80      0.87      0.83      1000\n",
+            "        frog       0.96      0.87      0.91      1000\n",
+            "       horse       0.94      0.94      0.94      1000\n",
+            "        ship       0.95      0.95      0.95      1000\n",
+            "       truck       0.92      0.94      0.93      1000\n",
+            "\n",
+            "    accuracy                           0.90     10000\n",
+            "   macro avg       0.90      0.90      0.90     10000\n",
+            "weighted avg       0.90      0.90      0.90     10000\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "pretrained = True -> False로 변경"
+      ],
+      "metadata": {
+        "id": "XqqOj6-NLI52"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import timm                      # ResNet, ViT, EfficientNet 등 다양한 최신 pretrained 모델을 쉽게 불러올 수 있음\n",
+        "\n",
+        "# ResNet도 불러올 수 있습니다! (ResNet은 shortcut 구조를 직접 보는게 좋을 것 같아서 설계해봤습니다 ㅎㅎ!)\n",
+        "model = timm.create_model(\n",
+        "    \"vit_tiny_patch16_224\",      # 사용할 ViT 모델 구조 (실습 시간 줄이기 위해 tiny 버전, patch size 16, 기본 입력 224)\n",
+        "    pretrained=False,             # 사전학습된 가중치 사용 여부 -> false로 변경\n",
+        "    img_size=32,                 # 입력 이미지 크기 (CIFAR-10: 32x32이기 때문. Resize augmentation을 하셔도 됩니다!)\n",
+        "    patch_size=4,                # 이미지를 4x4 patch로 분할\n",
+        "    num_classes=10               # 최종 분류 클래스 수 (CIFAR-10)\n",
+        ")\n",
+        "model = model.to(device)         # 모델을 GPU 또는 CPU로 이동"
+      ],
+      "metadata": {
+        "id": "yeNVCQiSKzUO"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "num_epoch = 10                  # epoch = 3 으로 줄여볼게요..! (나중에 혼자 실습할 때는 10 이상으로 설정해주세요~)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = torch.optim.AdamW(      # AdamW optimizer 사용 (Vision Transformer에 자주 사용됨)\n",
+        "    model.parameters(),             # 학습할 모든 파라미터\n",
+        "    lr=3e-4,                        # 3e-4 = 0.0003\n",
+        "    weight_decay=0.05               # weight decay (L2 regularization) → 과적합 방지\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "OPTuSegcK9e6"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_val_loss = float('inf')\n",
+        "patience = 5\n",
+        "patience_counter = 0\n",
+        "\n",
+        "train_losses, val_losses = [], []\n",
+        "train_accuracies, val_accuracies = [], []\n",
+        "\n",
+        "for epoch in range(1, num_epoch + 1):\n",
+        "    train_loss, train_acc = train(model, train_loader, optimizer, criterion, device)\n",
+        "    val_loss, val_acc = evaluate(model, test_loader, criterion, device)\n",
+        "\n",
+        "    train_losses.append(train_loss)\n",
+        "    val_losses.append(val_loss)\n",
+        "    train_accuracies.append(train_acc)\n",
+        "    val_accuracies.append(val_acc)\n",
+        "\n",
+        "    print(f\"\\n[Epoch {epoch}]\")\n",
+        "    print(f\"Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.2f}%\")\n",
+        "    print(f\"Val Loss:   {val_loss:.4f}, Val Accuracy:   {val_acc:.2f}%\\n\")\n",
+        "\n",
+        "    # Early Stopping\n",
+        "    if val_loss < best_val_loss:\n",
+        "        best_val_loss = val_loss\n",
+        "        best_model_state = model.state_dict()\n",
+        "        patience_counter = 0\n",
+        "    else:\n",
+        "        patience_counter += 1\n",
+        "        if patience_counter >= patience:\n",
+        "            print(f\"Early stopping triggered at epoch {epoch}\")\n",
+        "            break"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qBoG_b0CK-Fa",
+        "outputId": "849a8bf8-7744-40ce-8681-fc4d94b31bf6"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 1]\n",
+            "Train Loss: 1.7760, Train Accuracy: 33.60%\n",
+            "Val Loss:   1.5436, Val Accuracy:   43.31%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 2]\n",
+            "Train Loss: 1.4873, Train Accuracy: 45.52%\n",
+            "Val Loss:   1.4237, Val Accuracy:   48.51%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 3]\n",
+            "Train Loss: 1.3416, Train Accuracy: 51.10%\n",
+            "Val Loss:   1.3197, Val Accuracy:   51.76%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 4]\n",
+            "Train Loss: 1.2456, Train Accuracy: 54.66%\n",
+            "Val Loss:   1.2434, Val Accuracy:   54.73%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 5]\n",
+            "Train Loss: 1.1693, Train Accuracy: 57.92%\n",
+            "Val Loss:   1.1778, Val Accuracy:   57.81%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 6]\n",
+            "Train Loss: 1.1003, Train Accuracy: 60.51%\n",
+            "Val Loss:   1.0810, Val Accuracy:   60.25%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 7]\n",
+            "Train Loss: 1.0457, Train Accuracy: 62.44%\n",
+            "Val Loss:   1.0707, Val Accuracy:   61.12%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 8]\n",
+            "Train Loss: 0.9897, Train Accuracy: 64.46%\n",
+            "Val Loss:   1.0105, Val Accuracy:   64.07%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 9]\n",
+            "Train Loss: 0.9354, Train Accuracy: 66.81%\n",
+            "Val Loss:   0.9707, Val Accuracy:   65.40%\n",
+            "\n",
+            "Progress: |██████████████████████████████| 1563/1563 batches\n",
+            "\n",
+            "[Epoch 10]\n",
+            "Train Loss: 0.8918, Train Accuracy: 68.29%\n",
+            "Val Loss:   0.9516, Val Accuracy:   66.02%\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "epochs = range(1, len(train_losses) + 1)\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, val_losses, label='Validation Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.title('Loss Curve')\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(epochs, train_accuracies, label='Train Acc')\n",
+        "plt.plot(epochs, val_accuracies, label='Validation Acc')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy (%)')\n",
+        "plt.legend()\n",
+        "plt.title('Accuracy Curve')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "71OWekTnLBl0",
+        "outputId": "229becf5-9536-4c1f-c75e-698923fce835"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "all_preds = []\n",
+        "all_labels = []\n",
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "    for images, labels in test_loader:\n",
+        "        images, labels = images.to(device), labels.to(device)\n",
+        "        outputs = model(images)\n",
+        "        _, predicted = torch.max(outputs, 1)\n",
+        "        all_preds.extend(predicted.cpu().numpy())\n",
+        "        all_labels.extend(labels.cpu().numpy())\n",
+        "\n",
+        "cm = confusion_matrix(all_labels, all_preds, normalize='true')\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues',\n",
+        "            xticklabels=class_names, yticklabels=class_names)\n",
+        "plt.xlabel('Predicted Label')\n",
+        "plt.ylabel('True Label')\n",
+        "plt.title('Confusion Matrix')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "report = classification_report(all_labels, all_preds, target_names=class_names)\n",
+        "print(\"Classification Report:\\n\", report)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 941
+        },
+        "id": "EwcaaSlcLEtA",
+        "outputId": "c66b2d4c-eb22-4061-d274-67ed4fc75d02"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "    airplane       0.67      0.74      0.70      1000\n",
+            "  automobile       0.72      0.83      0.77      1000\n",
+            "        bird       0.58      0.58      0.58      1000\n",
+            "         cat       0.46      0.45      0.46      1000\n",
+            "        deer       0.75      0.43      0.55      1000\n",
+            "         dog       0.49      0.71      0.58      1000\n",
+            "        frog       0.76      0.73      0.74      1000\n",
+            "       horse       0.65      0.76      0.70      1000\n",
+            "        ship       0.88      0.71      0.79      1000\n",
+            "       truck       0.82      0.66      0.73      1000\n",
+            "\n",
+            "    accuracy                           0.66     10000\n",
+            "   macro avg       0.68      0.66      0.66     10000\n",
+            "weighted avg       0.68      0.66      0.66     10000\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##※ 과제 안내 ※\n",
+        "1. 이번 세션에서 다룬 Vision Transformer는 최근 이미지 분류 분야에서 ResNet을 뛰어넘는 성능을 보이며 SOTA(State-of-the-Art) 모델로 자리 잡았음을 배웠습니다. 그럼에도 불구하고 ResNet은 여전히 다양한 비전 응용 분야에서 널리 활용되고 있는데요! ViT와 비교했을 때 ResNet이 갖는 장점 한 가지 이상을 GitHub 과제 제출 시 **Pull Request 내용란에 작성**해주세요~ (ChatGPT로 정답을 적어도 좋지만.. 일차원적인 본인 생각이라도 적어주셨으면 좋겠습니다! 😋😋)\n",
+        "\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "2. 위 코드에서 pretrained = True ViT 모델을 불러왔는데요! False로 설정한다면 어떤 결과가 나올지 직접 코드를 돌리면서 확인해보면 좋을 것 같아요~ 어떤 차이가 있을지 직접 결과를 확인한 후 성능이 그렇게 나온 이유를 위와 마찬가지로 **Pull Request 내용란에 작성**해주세요~ (Hint: 초대형 어쩌구.. 🧐🧐)\n",
+        "\n",
+        "\n",
+        "---\n",
+        "\n",
+        "3. (선택) 이로써 자연어처리(NLP)와 컴퓨터비전(CV)의 기초&심화 세션을 마쳤습니다! 지금까지 어렵거나 아쉬웠던 내용 등 26기 BASE 커리큘럼 평가를 **위 과제와 함께 PR로** 편하게 남겨주시면 감사하겠습니다. (다음 기수 내용 수정 / 난이도 조절을 위해.. 딱히 없으면 안 적어도 됨 👍👍)"
+      ],
+      "metadata": {
+        "id": "xFQsDyjvLeWH"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 마치며\n",
+        "\n",
+        "다음 주 세션은 잠시 정규 트랙을 벗어나 **자유 주제**로 진행됩니다.\n",
+        "25기 부원분들께서 유익한 주제를 준비해주셨다고 하니 꼭 오셔서 재미있게 함께해 주세요 🙂🙂\n",
+        "\n",
+        "---\n",
+        "\n",
+        "그리고 다다음 주, 7주차 세션에서는 **미니프로젝트 1 최종 발표**가 예정되어 있습니다.\n",
+        "그동안 준비하신 결과를 공유하는 자리인 만큼, 여러분들의 발표를 기대하겠습니다! 😊😊"
+      ],
+      "metadata": {
+        "id": "fdWZlS0O1Oc1"
+      }
+    }
+  ]
+}
\ No newline at end of file