cat-or-dog-classification-with-sklearn-pytorch.ipynb

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   "source": [
    "## Cat and Dog Classification (SKlearn,OpenCV) and (PyTorch)"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "**1. Image Classification using OpenCV andSKlearn"
   ]
  },
  {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "train_folder = \"/kaggle/input/cats-and-dogs-image-classification/train\"\n",
    "test_folder = \"/kaggle/input/cats-and-dogs-image-classification/test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a534bc82",
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   "source": [
    "def load_images(folder):\n",
    "    images = []\n",
    "    labels = []\n",
    "    \n",
    "    for subfolder in os.listdir(folder):\n",
    "        subfolder_path = os.path.join(folder, subfolder)\n",
    "        label = 1 if subfolder == 'dogs' else 0\n",
    "        \n",
    "        for imagelist in os.listdir(subfolder_path):\n",
    "            image_path = os.path.join(subfolder_path, imagelist)\n",
    "            image = cv2.imread(image_path)\n",
    "            image = cv2.resize(image, (64, 64))\n",
    "            images.append(image)\n",
    "            labels.append(label)\n",
    "                \n",
    "    return np.array(images), np.array(labels)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "958d0e4d",
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   "outputs": [],
   "source": [
    "train_images, train_labels = load_images(train_folder)\n",
    "test_images, test_labels = load_images(test_folder)\n",
    "\n",
    "flat_train_images = train_images.reshape(train_images.shape[0], -1)\n",
    "flat_test_images = test_images.reshape(test_images.shape[0], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0fea816",
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    "execution": {
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   },
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   "source": [
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(max_iter=1000, random_state=seed),\n",
    "    'Random Forest': RandomForestClassifier(random_state=seed),\n",
    "    'Extra Trees': ExtraTreesClassifier(random_state=seed),\n",
    "    'Decision Tree': DecisionTreeClassifier(random_state=seed),\n",
    "    'XGBoost': xgb.XGBClassifier(random_state=seed),\n",
    "    'LightGBM': lgb.LGBMClassifier(random_state=seed)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "235509cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T10:57:59.278154Z",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score of Logistic Regression model: 0.6142857142857143\n",
      "Accuracy Score of Random Forest model: 0.5928571428571429\n",
      "Accuracy Score of Extra Trees model: 0.6\n",
      "Accuracy Score of Decision Tree model: 0.6071428571428571\n",
      "Accuracy Score of XGBoost model: 0.6571428571428571\n",
      "Accuracy Score of LightGBM model: 0.6071428571428571\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = {}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    model.fit(flat_train_images, train_labels)\n",
    "    pred_labels = model.predict(flat_test_images)\n",
    "    accuracy = accuracy_score(test_labels, pred_labels)\n",
    "    accuracy_scores[model_name] = accuracy\n",
    "    print(f\"Accuracy Score of {model_name} model: {accuracy}\")"
   ]
  },
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   "cell_type": "code",
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of Accuracy Scores\n",
    "accuracy_scores = {k: v * 100 for k, v in accuracy_scores.items()}\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x=list(accuracy_scores.values()), y=list(accuracy_scores.keys()), palette='tab10')\n",
    "plt.xlim(0, 100)\n",
    "plt.xlabel('Accuracy Score (%)')\n",
    "plt.ylabel('ML Models')\n",
    "plt.title('Accuracy Scores of Different Models', fontsize=10, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d73a7224",
   "metadata": {
    "papermill": {
     "duration": 0.007693,
     "end_time": "2023-08-25T10:59:10.978746",
     "exception": false,
     "start_time": "2023-08-25T10:59:10.971053",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "** 2. Image Classification using PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fce97652",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T10:59:10.996767Z",
     "iopub.status.busy": "2023-08-25T10:59:10.996347Z",
     "iopub.status.idle": "2023-08-25T10:59:15.046049Z",
     "shell.execute_reply": "2023-08-25T10:59:15.045150Z"
    },
    "papermill": {
     "duration": 4.06188,
     "end_time": "2023-08-25T10:59:15.048622",
     "exception": false,
     "start_time": "2023-08-25T10:59:10.986742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "np.random.seed(seed)\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64cbc780",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T10:59:15.067670Z",
     "iopub.status.busy": "2023-08-25T10:59:15.066592Z",
     "iopub.status.idle": "2023-08-25T10:59:15.071256Z",
     "shell.execute_reply": "2023-08-25T10:59:15.070402Z"
    },
    "papermill": {
     "duration": 0.016614,
     "end_time": "2023-08-25T10:59:15.073446",
     "exception": false,
     "start_time": "2023-08-25T10:59:15.056832",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dir = \"/kaggle/input/cats-and-dogs-image-classification/train\"\n",
    "test_dir = \"/kaggle/input/cats-and-dogs-image-classification/test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d60bacfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T10:59:15.091619Z",
     "iopub.status.busy": "2023-08-25T10:59:15.090958Z",
     "iopub.status.idle": "2023-08-25T10:59:15.106178Z",
     "shell.execute_reply": "2023-08-25T10:59:15.105315Z"
    },
    "papermill": {
     "duration": 0.026832,
     "end_time": "2023-08-25T10:59:15.108433",
     "exception": false,
     "start_time": "2023-08-25T10:59:15.081601",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_transform = transforms.Compose([\n",
    "    transforms.Resize(size=(64, 64)),\n",
    "    transforms.ToTensor()\n",
    "])\n",
    "\n",
    "train_data = datasets.ImageFolder(root=train_dir, transform=data_transform)\n",
    "test_data = datasets.ImageFolder(root=test_dir, transform=data_transform)\n",
    "\n",
    "train_dataloader = DataLoader(dataset=train_data, batch_size=32, shuffle=True)\n",
    "test_dataloader = DataLoader(dataset=test_data, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69764b5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T10:59:15.126751Z",
     "iopub.status.busy": "2023-08-25T10:59:15.126136Z",
     "iopub.status.idle": "2023-08-25T10:59:15.136231Z",
     "shell.execute_reply": "2023-08-25T10:59:15.135391Z"
    },
    "papermill": {
     "duration": 0.022167,
     "end_time": "2023-08-25T10:59:15.138709",
     "exception": false,
     "start_time": "2023-08-25T10:59:15.116542",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CNNModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNNModel, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2)\n",
    "        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2)\n",
    "        self.fc1 = nn.Linear(32 * 16 * 16, 128)  # Adjust the input size based on your architecture\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(128, 2)  # Output size 2 for binary classification\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.conv1(x)\n",
    "        out = self.relu1(out)\n",
    "        out = self.pool1(out)\n",
    "        out = self.conv2(out)\n",
    "        out = self.relu2(out)\n",
    "        out = self.pool2(out)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.fc1(out)\n",
    "        out = self.relu3(out)\n",
    "        out = self.fc2(out)\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "156115a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T10:59:15.157092Z",
     "iopub.status.busy": "2023-08-25T10:59:15.156484Z",
     "iopub.status.idle": "2023-08-25T10:59:15.217625Z",
     "shell.execute_reply": "2023-08-25T10:59:15.216487Z"
    },
    "papermill": {
     "duration": 0.073558,
     "end_time": "2023-08-25T10:59:15.220393",
     "exception": false,
     "start_time": "2023-08-25T10:59:15.146835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = CNNModel().to(device)\n",
    "\n",
    "error = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "epochs = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d82f0251",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T10:59:15.239099Z",
     "iopub.status.busy": "2023-08-25T10:59:15.238713Z",
     "iopub.status.idle": "2023-08-25T11:02:29.970429Z",
     "shell.execute_reply": "2023-08-25T11:02:29.968943Z"
    },
    "papermill": {
     "duration": 194.743956,
     "end_time": "2023-08-25T11:02:29.972979",
     "exception": false,
     "start_time": "2023-08-25T10:59:15.229023",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/25] Loss: 12.5592\n",
      "Epoch [2/25] Loss: 12.3908\n",
      "Epoch [3/25] Loss: 12.0683\n",
      "Epoch [4/25] Loss: 11.7825\n",
      "Epoch [5/25] Loss: 10.8446\n",
      "Epoch [6/25] Loss: 10.7336\n",
      "Epoch [7/25] Loss: 9.8519\n",
      "Epoch [8/25] Loss: 8.8490\n",
      "Epoch [9/25] Loss: 8.8041\n",
      "Epoch [10/25] Loss: 7.5051\n",
      "Epoch [11/25] Loss: 6.6745\n",
      "Epoch [12/25] Loss: 6.1198\n",
      "Epoch [13/25] Loss: 5.0967\n",
      "Epoch [14/25] Loss: 5.3614\n",
      "Epoch [15/25] Loss: 4.2980\n",
      "Epoch [16/25] Loss: 4.1279\n",
      "Epoch [17/25] Loss: 2.7752\n",
      "Epoch [18/25] Loss: 2.1707\n",
      "Epoch [19/25] Loss: 1.8268\n",
      "Epoch [20/25] Loss: 1.5070\n",
      "Epoch [21/25] Loss: 1.0293\n",
      "Epoch [22/25] Loss: 0.8739\n",
      "Epoch [23/25] Loss: 0.8715\n",
      "Epoch [24/25] Loss: 0.8660\n",
      "Epoch [25/25] Loss: 0.5612\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    for images, labels in train_dataloader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = error(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "    print(f\"Epoch [{epoch+1}/{epochs}] Loss: {epoch_loss:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "97fcd30a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T11:02:29.995839Z",
     "iopub.status.busy": "2023-08-25T11:02:29.995405Z",
     "iopub.status.idle": "2023-08-25T11:02:31.654535Z",
     "shell.execute_reply": "2023-08-25T11:02:31.653163Z"
    },
    "papermill": {
     "duration": 1.673598,
     "end_time": "2023-08-25T11:02:31.657125",
     "exception": false,
     "start_time": "2023-08-25T11:02:29.983527",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of CNN: 0.64\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "predictions = []\n",
    "true_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_dataloader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        predictions.extend(predicted.cpu().numpy())\n",
    "        true_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "accuracy_cnn = accuracy_score(true_labels, predictions)\n",
    "print(f\"Accuracy of CNN: {accuracy_cnn:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b58fb3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-25T11:02:31.680286Z",
     "iopub.status.busy": "2023-08-25T11:02:31.679892Z",
     "iopub.status.idle": "2023-08-25T11:02:31.895216Z",
     "shell.execute_reply": "2023-08-25T11:02:31.894139Z"
    },
    "papermill": {
     "duration": 0.23032,
     "end_time": "2023-08-25T11:02:31.897938",
     "exception": false,
     "start_time": "2023-08-25T11:02:31.667618",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.barplot(x=[accuracy_cnn * 100], y=['CNN'], palette='tab10')\n",
    "plt.xlim(0, 100)\n",
    "plt.xlabel('Accuracy Score (%)')\n",
    "plt.ylabel('Models')\n",
    "plt.title('Accuracy Score of CNN', fontsize=10, fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab513ab2",
   "metadata": {
    "papermill": {
     "duration": 0.01038,
     "end_time": "2023-08-25T11:02:31.919662",
     "exception": false,
     "start_time": "2023-08-25T11:02:31.909282",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Conclusion**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa28398",
   "metadata": {
    "papermill": {
     "duration": 0.010366,
     "end_time": "2023-08-25T11:02:31.940667",
     "exception": false,
     "start_time": "2023-08-25T11:02:31.930301",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The cats and dogs image dataset is preprocessed, feature extracted and evaluated with SKlearn package and compared with a custom model using CNN in PyTorch framework.\n",
    "\n",
    "* Accuracy (SKlearn) - **XGBoost**  = **65.71%**\n",
    "* Accuracy (PyTorch) - **CNN**      = **64 %**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fc0fefc",
   "metadata": {
    "papermill": {
     "duration": 0.01053,
     "end_time": "2023-08-25T11:02:31.961844",
     "exception": false,
     "start_time": "2023-08-25T11:02:31.951314",
     "status": "completed"
    },
    "tags": []
   },
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