Step_1_Model_archi.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"J7VBfWep9534"},"source":["# CNN Implementation : step 1, selecting the best model architecture"]},{"cell_type":"markdown","metadata":{"id":"cwqARc1oORiA"},"source":["Necessary imports :"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5DL4pHZgOQoY"},"outputs":[],"source":["from google.colab import drive\n","import zipfile\n","import os\n","\n","import torch\n","from torchvision import datasets, transforms, models\n","from torch.utils.data import DataLoader, WeightedRandomSampler\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import torch.nn as nn\n","import torch.optim as optim\n","from sklearn.metrics import f1_score\n","from torchvision.models import resnet18, resnet50,  googlenet, inception_v3, mobilenet_v2, mobilenet_v3_small, mobilenet_v3_large, efficientnet_v2_m\n"]},{"cell_type":"markdown","metadata":{"id":"3NzSMUdC-Bgi"},"source":["## Data importation"]},{"cell_type":"markdown","metadata":{"id":"3QaVliha4Dtp"},"source":["Allow this notebook access to google drive:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20262,"status":"ok","timestamp":1734170239780,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"LnkNP06S04CB","outputId":"0f154d3e-b6d9-4402-a8eb-c0b225f1a30e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["# Use drive.mount with force_remount=True\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"t98Onnpn4RqH"},"source":["Extract the data from the zip file in the drive, to the Colab working directory (~40s runtime) :"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bB1MLfUW2abF"},"outputs":[],"source":["# Path to your ZIP file in Google Drive\n","zip_path = '/content/drive/MyDrive/ML Project 2 :)/data (2).zip'\n","\n","# Extract to a working directory\n","extract_path = '/content/dataset'\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(extract_path)"]},{"cell_type":"markdown","metadata":{"id":"J_Oh9J0EryhY"},"source":["Normalize the data, and create the training and validation sets"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":875,"status":"ok","timestamp":1734170277624,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"4IC9ISQr4C7m","outputId":"bda88678-e3fe-497d-ecbd-4cc49768d954"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of an image after transformation: torch.Size([3, 224, 224])\n"]}],"source":["# Define the final transformation pipeline with normalization (optional)\n","transform = transforms.Compose([\n","    transforms.Resize((224, 224)),  # Resize the image to 224x224 for ResNet\n","    transforms.ToTensor(),  # Convert to Tensor\n","    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # Normalize with ImageNet stats to use pretrained weights\n","])\n","\n","# Load the datasets again using the final transformation\n","train_dataset = datasets.ImageFolder(os.path.join(extract_path, 'train'), transform=transform)\n","val_dataset = datasets.ImageFolder(os.path.join(extract_path, 'validation'), transform=transform)\n","\n","# Check the shape of an image after transformation\n","first_image, first_label = train_dataset[0]\n","print(f\"Shape of an image after transformation: {first_image.shape}\")"]},{"cell_type":"markdown","metadata":{"id":"EcauuaVYIFEi"},"source":["## Exploring multiple architectures"]},{"cell_type":"markdown","metadata":{"id":"zWFgfx6RItI0"},"source":["We want to find what architecture is best suited for our classification task. We are going to try different models, with pretrained weights and 5 epochs. We use a basic Adam optimizer with learning rate 0.001 for all models, as it is a good strategy for many models. A batch size of 32 is a good compromise between speed and convergence.\n","\n","We choose to use pretrained weights because after a quick test conducted on ResNet50 with 2 epochs, we estimate that it will provide better results (see CNN_my_norm.ipynb and CNN_pretraining.ipynb). Consequently, the right standardisation (based on ImageNet) and resizing to 224x224 has been done.\n","\n","We choose 5 epochs because it should be sufficient to start observing what model will perform better generally, and we want to make good use of our computational time.\n","\n","The model selection started from ResNet and GoogLeNet, quite classical models for computer vision, and MobileNet and EfficientNet as more varied models. Then according to the observed performance, we chose other versions of GoogLeNet (Inception) and MobileNet."]},{"cell_type":"markdown","metadata":{"id":"bmW9UDEa6Fzg"},"source":["### Useful functions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NYO2vKsXMYxR"},"outputs":[],"source":["# Define number of epochs\n","num_epochs = 5\n","\n","# Define loss\n","criterion = nn.CrossEntropyLoss()\n","\n","# Create dataloaders\n","train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n","val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)"]},{"cell_type":"markdown","metadata":{"id":"5E7HM45EiJli"},"source":["Default training function :"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LNen9iK8iIv2"},"outputs":[],"source":["def default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs):\n","    for epoch in range(num_epochs):\n","        model.train()\n","        running_loss = 0.0\n","        for images, labels in train_loader:\n","            images, labels = images.to(device), labels.to(device)\n","\n","            optimizer.zero_grad()\n","            outputs = model(images)\n","            loss = criterion(outputs, labels)\n","            loss.backward()\n","            optimizer.step()\n","\n","            running_loss += loss.item()\n","        print(f\"Default Training - Epoch {epoch+1}, Loss: {running_loss/len(train_loader)}\")"]},{"cell_type":"markdown","metadata":{"id":"IekQ-0PVafmF"},"source":["A function to evaluate performance on the validation set, basis for our comparisons :"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CfwYg_hsaeWc"},"outputs":[],"source":["def evaluate_model(model, val_loader, criterion, device):\n","    \"\"\"\n","    Evaluates the model on a validation dataset.\n","\n","    Args:\n","        model (torch.nn.Module): The model to evaluate.\n","        val_loader (torch.utils.data.DataLoader): DataLoader for the validation dataset.\n","        criterion (torch.nn.Module): Loss function.\n","        device (torch.device): Device to run the evaluation on (CPU or GPU).\n","\n","    Returns:\n","        dict: A dictionary containing validation loss, accuracy, and F1 score.\n","    \"\"\"\n","    model.eval()  # Set model to evaluation mode\n","\n","    correct = 0\n","    total = 0\n","    val_loss = 0.0\n","    all_labels = []\n","    all_predictions = []\n","\n","    with torch.no_grad():  # Disable gradient calculation for inference\n","        for images, labels in val_loader:\n","            images, labels = images.to(device), labels.to(device)\n","\n","            # Forward pass\n","            outputs = model(images)\n","            loss = criterion(outputs, labels)\n","            val_loss += loss.item()\n","\n","            # Get predictions\n","            _, predicted = torch.max(outputs, 1)\n","            total += labels.size(0)\n","            correct += (predicted == labels).sum().item()\n","\n","            # Collect all labels and predictions for F1 score\n","            all_labels.extend(labels.cpu().numpy())       # Convert to numpy and store\n","            all_predictions.extend(predicted.cpu().numpy())\n","\n","    # Calculate accuracy and average loss\n","    accuracy = correct / total\n","    avg_val_loss = val_loss / len(val_loader)\n","\n","    # Compute F1 Score\n","    f1 = f1_score(all_labels, all_predictions, average=\"weighted\")  # Weighted for class imbalance\n","\n","    # Return results as a dictionary\n","    return {\n","        \"validation_loss\": avg_val_loss,\n","        \"accuracy\": accuracy * 100,  # Percentage\n","        \"f1_score\": f1\n","    }"]},{"cell_type":"markdown","metadata":{"id":"hCPE7VR6yini"},"source":["### ResNet18"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":261,"status":"ok","timestamp":1732817391863,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"Sc423OW1ykEo","outputId":"f13185b1-71f3-4a42-ec1f-75b557b7a261"},"outputs":[{"data":{"text/plain":["ResNet(\n","  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace=True)\n","  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","  (layer1): Sequential(\n","    (0): 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","      (relu): ReLU(inplace=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","    )\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","      (relu): ReLU(inplace=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","    )\n","  )\n","  (layer2): 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","      (relu): ReLU(inplace=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","      (downsample): 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","      (relu): ReLU(inplace=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","    )\n","  )\n","  (layer3): Sequential(\n","    (0): BasicBlock(\n","      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): BasicBlock(\n","      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): BasicBlock(\n","      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): BasicBlock(\n","      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (fc): Linear(in_features=512, out_features=2, bias=True)\n",")"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["# Load ResNet model with the updated 'weights' parameter\n","model = resnet18(weights=\"IMAGENET1K_V1\")\n","model.fc = nn.Linear(model.fc.in_features, 2)  # Adjust the final layer for 2 classes\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Suppress model summary or logs by not printing it\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1249920,"status":"ok","timestamp":1732818649420,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"W9QHASbcynbI","outputId":"5543fa24-bef4-47e4-9015-031b4c77530f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default Training - Epoch 1, Loss: 0.2335220359972856\n","Default Training - Epoch 2, Loss: 0.18138559385803163\n","Default Training - Epoch 3, Loss: 0.15749037644669514\n","Default Training - Epoch 4, Loss: 0.12898822454336173\n","Default Training - Epoch 5, Loss: 0.10008126241083636\n"]}],"source":["default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WuFnGZ-xyo9p"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet18_3rdRun.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":33291,"status":"ok","timestamp":1732818683093,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"4jDi_ATnyq7g","outputId":"f93d86fc-a387-4c67-dd9d-8bb0ab3466dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.2525\n","Accuracy: 91.45%\n","F1 Score: 0.9117\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet18_3rdRun.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"_0aHpLq4dUgg"},"source":["### ResNet50"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":781,"status":"ok","timestamp":1732654325161,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"5mtj7GpWL-SQ","outputId":"39532252-1a06-48f5-d7f2-597d82c03920"},"outputs":[{"data":{"text/plain":["ResNet(\n","  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace=True)\n","  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","  (layer1): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(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","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(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","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(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","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer2): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(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=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(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","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(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","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(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","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer3): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (4): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (5): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (fc): Linear(in_features=2048, out_features=2, bias=True)\n",")"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Load ResNet model with the updated 'weights' parameter\n","model = resnet50(weights=\"IMAGENET1K_V1\")\n","model.fc = nn.Linear(model.fc.in_features, 2)  # Adjust the final layer for 2 classes\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Suppress model summary or logs by not printing it\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"markdown","metadata":{"id":"m-jwcLiXNa5g"},"source":["Training :"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":83001,"status":"ok","timestamp":1732657324957,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"35Iq3GcZLx7I","outputId":"b6eae871-f699-4727-b3cd-0da2f0368277"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default Training - Epoch 1, Loss: 0.233046756510043\n","Default Training - Epoch 2, Loss: 0.19334352272676816\n","Default Training - Epoch 3, Loss: 0.1738665737166051\n","Default Training - Epoch 4, Loss: 0.15802769290794635\n","Default Training - Epoch 5, Loss: 0.1443929637179655\n"]}],"source":["default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"markdown","metadata":{"id":"EeeomOikNdfX"},"source":["Saving weights :"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rDS6VibPlnWW"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet50.pth')"]},{"cell_type":"markdown","metadata":{"id":"Wt8wdT3ENhbI"},"source":["Evaluating performance :"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":49559,"status":"ok","timestamp":1732657375026,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"mDdSoHy8lox4","outputId":"266aec67-9827-4b0b-9806-e6eb6defbb2c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.2696\n","Accuracy: 91.06%\n","F1 Score: 0.8973\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet50.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"FuT4DvBCoS3w"},"source":["### GoogLeNet"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":815,"status":"ok","timestamp":1732820219611,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"H88E_8RmoXSs","outputId":"754ec135-0c95-4890-813b-f71e550b890a"},"outputs":[{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/googlenet-1378be20.pth\" to /root/.cache/torch/hub/checkpoints/googlenet-1378be20.pth\n","100%|██████████| 49.7M/49.7M [00:00<00:00, 174MB/s]\n"]},{"data":{"text/plain":["GoogLeNet(\n","  (conv1): BasicConv2d(\n","    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n","  (conv2): BasicConv2d(\n","    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (conv3): BasicConv2d(\n","    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n","  (inception3a): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (inception3b): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n","  (inception4a): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (inception4b): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (inception4c): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (inception4d): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (inception4e): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n","  (inception5a): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (inception5b): Inception(\n","    (branch1): BasicConv2d(\n","      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch2): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch3): Sequential(\n","      (0): BasicConv2d(\n","        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (1): BasicConv2d(\n","        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (branch4): Sequential(\n","      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n","      (1): BasicConv2d(\n","        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","  )\n","  (aux1): None\n","  (aux2): None\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (dropout): Dropout(p=0.2, inplace=False)\n","  (fc): Linear(in_features=1024, out_features=2, bias=True)\n",")"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["# Load Inception-ResNet V2 model with the updated 'weights' parameter\n","model = googlenet(weights=\"IMAGENET1K_V1\")\n","model.fc = nn.Linear(model.fc.in_features, 2)\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1378905,"status":"ok","timestamp":1732745098197,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"jrNsP27wpJMy","outputId":"fb28c265-3d08-4c81-d930-a308eecdf11d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default Training - Epoch 1, Loss: 0.20341019775471925\n","Default Training - Epoch 2, Loss: 0.1636338727617198\n","Default Training - Epoch 3, Loss: 0.13539876616571336\n","Default Training - Epoch 4, Loss: 0.1117715378686804\n","Default Training - Epoch 5, Loss: 0.09653054518732425\n"]}],"source":["default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8IfoIK2epMMb"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/Googlenet.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":36178,"status":"ok","timestamp":1732820264138,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"csPhMg1IpSFQ","outputId":"f8ba8a78-7c0f-4718-fb36-cfa7478e5eb4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.2933\n","Accuracy: 92.66%\n","F1 Score: 0.9165\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/Googlenet.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"uoYDCXNVWFlc"},"source":["### Inception V3"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":1503,"status":"ok","timestamp":1732701937169,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"pq-6wPg3Wonz","outputId":"a5667da4-b97f-40ff-f0d2-48d9fb4f8fe1"},"outputs":[{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\" to /root/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\n","100%|██████████| 104M/104M [00:00<00:00, 208MB/s] \n"]},{"data":{"text/plain":["Inception3(\n","  (Conv2d_1a_3x3): BasicConv2d(\n","    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)\n","    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (Conv2d_2a_3x3): BasicConv2d(\n","    (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)\n","    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (Conv2d_2b_3x3): BasicConv2d(\n","    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (Conv2d_3b_1x1): BasicConv2d(\n","    (conv): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","    (bn): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (Conv2d_4a_3x3): BasicConv2d(\n","    (conv): Conv2d(80, 192, kernel_size=(3, 3), stride=(1, 1), bias=False)\n","    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","  )\n","  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (Mixed_5b): InceptionA(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch5x5_1): BasicConv2d(\n","      (conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch5x5_2): BasicConv2d(\n","      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_1): BasicConv2d(\n","      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_2): BasicConv2d(\n","      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3): BasicConv2d(\n","      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_5c): InceptionA(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch5x5_1): BasicConv2d(\n","      (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch5x5_2): BasicConv2d(\n","      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_1): BasicConv2d(\n","      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_2): BasicConv2d(\n","      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3): BasicConv2d(\n","      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_5d): InceptionA(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch5x5_1): BasicConv2d(\n","      (conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch5x5_2): BasicConv2d(\n","      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_1): BasicConv2d(\n","      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_2): BasicConv2d(\n","      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3): BasicConv2d(\n","      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_6a): InceptionB(\n","    (branch3x3): BasicConv2d(\n","      (conv): Conv2d(288, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_1): BasicConv2d(\n","      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_2): BasicConv2d(\n","      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3): BasicConv2d(\n","      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)\n","      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_6b): InceptionC(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_1): BasicConv2d(\n","      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_2): BasicConv2d(\n","      (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_3): BasicConv2d(\n","      (conv): Conv2d(128, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_1): BasicConv2d(\n","      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_2): BasicConv2d(\n","      (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_3): BasicConv2d(\n","      (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_4): BasicConv2d(\n","      (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_5): BasicConv2d(\n","      (conv): Conv2d(128, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_6c): InceptionC(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_1): BasicConv2d(\n","      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_2): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_3): BasicConv2d(\n","      (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_1): BasicConv2d(\n","      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_2): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_3): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_4): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_5): BasicConv2d(\n","      (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_6d): InceptionC(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_1): BasicConv2d(\n","      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_2): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_3): BasicConv2d(\n","      (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_1): BasicConv2d(\n","      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_2): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_3): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_4): BasicConv2d(\n","      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_5): BasicConv2d(\n","      (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_6e): InceptionC(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_2): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7_3): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_2): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_3): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_4): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7dbl_5): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (AuxLogits): Sequential()\n","  (Mixed_7a): InceptionD(\n","    (branch3x3_1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3_2): BasicConv2d(\n","      (conv): Conv2d(192, 320, kernel_size=(3, 3), stride=(2, 2), bias=False)\n","      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7x3_1): BasicConv2d(\n","      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7x3_2): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7x3_3): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch7x7x3_4): BasicConv2d(\n","      (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_7b): InceptionE(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3_1): BasicConv2d(\n","      (conv): Conv2d(1280, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3_2a): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3_2b): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_1): BasicConv2d(\n","      (conv): Conv2d(1280, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_2): BasicConv2d(\n","      (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3a): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3b): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(1280, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (Mixed_7c): InceptionE(\n","    (branch1x1): BasicConv2d(\n","      (conv): Conv2d(2048, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3_1): BasicConv2d(\n","      (conv): Conv2d(2048, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3_2a): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3_2b): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_1): BasicConv2d(\n","      (conv): Conv2d(2048, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_2): BasicConv2d(\n","      (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3a): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch3x3dbl_3b): BasicConv2d(\n","      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n","      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","    (branch_pool): BasicConv2d(\n","      (conv): Conv2d(2048, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (dropout): Dropout(p=0.5, inplace=False)\n","  (fc): Linear(in_features=2048, out_features=2, bias=True)\n",")"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["# Load Inception-ResNet V2 model with the updated 'weights' parameter\n","model = inception_v3(weights=\"IMAGENET1K_V1\")  # Use IMAGENET1K_V1 weights\n","model.AuxLogits = nn.Sequential()  # Remove auxiliary classifier (optional, depending on your task)\n","\n","# Adjust the final layer for 2 classes (binary classification)\n","model.fc = nn.Linear(model.fc.in_features, 2)\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2323756,"status":"ok","timestamp":1732704274842,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"s1PSGy8XWyos","outputId":"0d977aca-221a-415f-d8f5-0b01937a50ac"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1, Loss: 0.2462647935482856\n","Epoch 2, Loss: 0.20834234960185474\n","Epoch 3, Loss: 0.19212795567685081\n","Epoch 4, Loss: 0.17072684607353272\n","Epoch 5, Loss: 0.15543333327984918\n"]}],"source":["import torchvision\n","\n","# We neglect the auxiliary classifiers : we shouldn't need them given our task (binary classifier)\n","# and our data (medium size)\n","def Inception_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs):\n","    for epoch in range(num_epochs):\n","        model.train()\n","        running_loss = 0.0\n","\n","        for inputs, targets in train_loader:\n","            inputs, targets = inputs.to(device), targets.to(device)\n","\n","            # Forward pass\n","            outputs = model(inputs)\n","\n","            # If the model returns InceptionOutputs, extract only the main output (logits)\n","            if isinstance(outputs, torchvision.models.inception.InceptionOutputs):\n","                outputs = outputs.logits  # Use only the main output\n","\n","            # Compute the loss using the main output\n","            loss = criterion(outputs, targets)\n","\n","            # Backward pass and optimization\n","            optimizer.zero_grad()\n","            loss.backward()\n","            optimizer.step()\n","\n","            running_loss += loss.item()\n","\n","        print(f\"Epoch {epoch+1}, Loss: {running_loss / len(train_loader)}\")\n","\n","Inception_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"markdown","metadata":{"id":"SIapcuwGTp-2"},"source":["\n","Running time : 50 min"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LbzuihLCW1Wo"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/InceptionV3.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":41954,"status":"ok","timestamp":1732704316792,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"KEOV443LW3Rt","outputId":"2c2fe381-1392-4f9b-f078-86ff4df0de34"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.2261\n","Accuracy: 91.91%\n","F1 Score: 0.9194\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/InceptionV3.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"H3MdtvYDnD5W"},"source":["### MobileNet V2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":1210,"status":"ok","timestamp":1732740879743,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"T6xU8YmgnCZG","outputId":"d57e0005-d53f-4408-cd6f-fc86f400fd8f"},"outputs":[{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/mobilenet_v2-b0353104.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth\n","100%|██████████| 13.6M/13.6M [00:00<00:00, 189MB/s]\n"]},{"data":{"text/plain":["MobileNetV2(\n","  (features): Sequential(\n","    (0): Conv2dNormActivation(\n","      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (2): ReLU6(inplace=True)\n","    )\n","    (1): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n","          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (2): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n","          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (3): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n","          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (4): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n","          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (5): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n","          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (6): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n","          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (7): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n","          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (8): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (9): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (10): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (11): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n","          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (12): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n","          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (13): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n","          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (14): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n","          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (15): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n","          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (16): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n","          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (17): InvertedResidual(\n","      (conv): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n","          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","          (2): ReLU6(inplace=True)\n","        )\n","        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (18): Conv2dNormActivation(\n","      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (2): ReLU6(inplace=True)\n","    )\n","  )\n","  (classifier): Sequential(\n","    (0): Dropout(p=0.2, inplace=False)\n","    (1): Linear(in_features=1280, out_features=2, bias=True)\n","  )\n",")"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# Load Inception-ResNet V2 model with the updated 'weights' parameter\n","model = mobilenet_v2(weights=\"IMAGENET1K_V1\")\n","model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, 2)\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":274446,"status":"ok","timestamp":1732742259902,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"qAwnB2WHnu_F","outputId":"abc3ab68-653d-4af3-bbde-dd12e334bf60"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default Training - Epoch 1, Loss: 0.20142519628679306\n","Default Training - Epoch 2, Loss: 0.15511047131696934\n","Default Training - Epoch 3, Loss: 0.13584342718697337\n","Default Training - Epoch 4, Loss: 0.11738043271607775\n","Default Training - Epoch 5, Loss: 0.0966200593902613\n"]}],"source":["default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dQ4ST5rKnyZA"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV2.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":30709,"status":"ok","timestamp":1732742290609,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"CXw9Ixr5n3GU","outputId":"47d37fb5-a948-42c2-df3f-31f581e08d69"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.2449\n","Accuracy: 91.77%\n","F1 Score: 0.9167\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV2.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"k9PbhYe3yy4b"},"source":["### MobileNet V3-S"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":378,"status":"ok","timestamp":1732749231811,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"AxAdeZvry2aK","outputId":"9758044e-fde2-4388-ba4e-ea847c846041"},"outputs":[{"data":{"text/plain":["MobileNetV3(\n","  (features): Sequential(\n","    (0): Conv2dNormActivation(\n","      (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","      (2): Hardswish()\n","    )\n","    (1): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=16, bias=False)\n","          (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(16, 8, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (2): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(16, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(72, 72, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=72, bias=False)\n","          (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (3): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(24, 88, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(88, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(88, 88, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=88, bias=False)\n","          (1): BatchNorm2d(88, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(88, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (4): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(24, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)\n","          (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(24, 96, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(96, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (5): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n","          (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(240, 64, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(64, 240, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (6): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n","          (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(240, 64, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(64, 240, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (7): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False)\n","          (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (8): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(48, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(144, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(144, 144, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=144, bias=False)\n","          (1): BatchNorm2d(144, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(40, 144, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (9): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(288, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(288, 288, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=288, bias=False)\n","          (1): BatchNorm2d(288, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(72, 288, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (10): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(576, 576, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=576, bias=False)\n","          (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(144, 576, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (11): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(576, 576, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=576, bias=False)\n","          (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(144, 576, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (12): Conv2dNormActivation(\n","      (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","      (2): Hardswish()\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=1)\n","  (classifier): Sequential(\n","    (0): Linear(in_features=576, out_features=1024, bias=True)\n","    (1): Hardswish()\n","    (2): Dropout(p=0.2, inplace=True)\n","    (3): Linear(in_features=1024, out_features=2, bias=True)\n","  )\n",")"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["# Load Inception-ResNet V2 model with the updated 'weights' parameter\n","model = mobilenet_v3_small(weights=\"IMAGENET1K_V1\")\n","model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, 2)\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":778708,"status":"ok","timestamp":1732750015165,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"Q3zdhoKNzAya","outputId":"6b7b46f3-0db6-4062-f15d-e11a6dd18ac1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default Training - Epoch 1, Loss: 0.16145696006024796\n","Default Training - Epoch 2, Loss: 0.11485405493187821\n","Default Training - Epoch 3, Loss: 0.0869539680958648\n","Default Training - Epoch 4, Loss: 0.06739243043468342\n","Default Training - Epoch 5, Loss: 0.05359866596037653\n"]}],"source":["default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0ArFtlTRzDhK"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3S.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24454,"status":"ok","timestamp":1732750039613,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"2oie70E1zHzr","outputId":"dfd53ffc-22dd-4855-f54d-f7fd0908ac1f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.5542\n","Accuracy: 90.46%\n","F1 Score: 0.8800\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3S_3.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"52WrRpSw-Uyw"},"source":["We observe some overfitting (low training loss, significantly higher validation loss)"]},{"cell_type":"markdown","metadata":{"id":"ZBba8Jh5fA2f"},"source":["### MobileNet V3-L"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":734,"status":"ok","timestamp":1732742346337,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"uHu7GtUTfAAw","outputId":"71716ca4-34db-4284-9b62-ddec7e2792d4"},"outputs":[{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_large-8738ca79.pth\n","100%|██████████| 21.1M/21.1M [00:00<00:00, 133MB/s] \n"]},{"data":{"text/plain":["MobileNetV3(\n","  (features): Sequential(\n","    (0): Conv2dNormActivation(\n","      (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","      (2): Hardswish()\n","    )\n","    (1): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=16, bias=False)\n","          (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (2): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False)\n","          (1): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(64, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (3): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)\n","          (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (4): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(72, 72, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=72, bias=False)\n","          (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(72, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (5): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False)\n","          (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (6): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False)\n","          (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): ReLU(inplace=True)\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (7): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)\n","          (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (8): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(80, 200, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(200, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(200, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=200, bias=False)\n","          (1): BatchNorm2d(200, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(200, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (9): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(80, 184, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(184, 184, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=184, bias=False)\n","          (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(184, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (10): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(80, 184, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(184, 184, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=184, bias=False)\n","          (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): Conv2dNormActivation(\n","          (0): Conv2d(184, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (11): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(480, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n","          (1): BatchNorm2d(480, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(480, 120, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(120, 480, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(112, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (12): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False)\n","          (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(672, 168, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(168, 672, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(112, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (13): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)\n","          (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(672, 168, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(168, 672, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(672, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (14): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)\n","          (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(960, 240, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(240, 960, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (15): InvertedResidual(\n","      (block): Sequential(\n","        (0): Conv2dNormActivation(\n","          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (1): Conv2dNormActivation(\n","          (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)\n","          (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","          (2): Hardswish()\n","        )\n","        (2): SqueezeExcitation(\n","          (avgpool): AdaptiveAvgPool2d(output_size=1)\n","          (fc1): Conv2d(960, 240, kernel_size=(1, 1), stride=(1, 1))\n","          (fc2): Conv2d(240, 960, kernel_size=(1, 1), stride=(1, 1))\n","          (activation): ReLU()\n","          (scale_activation): Hardsigmoid()\n","        )\n","        (3): Conv2dNormActivation(\n","          (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","        )\n","      )\n","    )\n","    (16): Conv2dNormActivation(\n","      (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n","      (2): Hardswish()\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=1)\n","  (classifier): Sequential(\n","    (0): Linear(in_features=960, out_features=1280, bias=True)\n","    (1): Hardswish()\n","    (2): Dropout(p=0.2, inplace=True)\n","    (3): Linear(in_features=1280, out_features=2, bias=True)\n","  )\n",")"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# Load Inception-ResNet V2 model with the updated 'weights' parameter\n","model = mobilenet_v3_large(weights=\"IMAGENET1K_V1\")\n","model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, 2)\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1095560,"status":"ok","timestamp":1732743501666,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"Mi1NscoCgqrv","outputId":"23d625ea-3e2c-488a-ea42-055d02bdcfcf"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default Training - Epoch 1, Loss: 0.16849345395081294\n","Default Training - Epoch 2, Loss: 0.11485232061657406\n","Default Training - Epoch 3, Loss: 0.08756794422850933\n","Default Training - Epoch 4, Loss: 0.0654483656013041\n","Default Training - Epoch 5, Loss: 0.046607730606098685\n"]}],"source":["default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xxUM_a8z7aEN"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3L.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":29703,"status":"ok","timestamp":1732743531369,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"L0d1of2O7SBA","outputId":"c1a5d758-4568-428a-a9ea-c0b4b14d7860"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.4976\n","Accuracy: 91.10%\n","F1 Score: 0.8881\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3L.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"F_uI6nDXv4Tr"},"source":["It is worth noting that this performs worse than MobileNet V2. We observe a very low training loss but a validation loss that is 10 times higher, therefore some overfitting might be at play. This could be due to the excessive complexity of the model for our task and data, compared to MobileNet V2."]},{"cell_type":"markdown","metadata":{"id":"DOQ1-0ZzphKc"},"source":["### EfficientNetV2-M"]},{"cell_type":"markdown","metadata":{"id":"Zye90t2zp3oO"},"source":["We choose size M for a good balance between accuracy and efficiency."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":1941,"status":"ok","timestamp":1732664177677,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"a4edN2IfqCEa","outputId":"0c98732b-e5f5-4cce-fffb-b62f43cd591f"},"outputs":[{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_v2_m-dc08266a.pth\n","100%|██████████| 208M/208M [00:01<00:00, 209MB/s]\n"]},{"data":{"text/plain":["EfficientNet(\n","  (features): Sequential(\n","    (0): Conv2dNormActivation(\n","      (0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      (2): SiLU(inplace=True)\n","    )\n","    (1): Sequential(\n","      (0): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.0, mode=row)\n","      )\n","      (1): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.0035087719298245615, mode=row)\n","      )\n","      (2): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.007017543859649123, mode=row)\n","      )\n","    )\n","    (2): Sequential(\n","      (0): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(24, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.010526315789473686, mode=row)\n","      )\n","      (1): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.014035087719298246, mode=row)\n","      )\n","      (2): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.017543859649122806, mode=row)\n","      )\n","      (3): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.02105263157894737, mode=row)\n","      )\n","      (4): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.024561403508771933, mode=row)\n","      )\n","    )\n","    (3): Sequential(\n","      (0): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(192, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.028070175438596492, mode=row)\n","      )\n","      (1): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.031578947368421054, mode=row)\n","      )\n","      (2): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.03508771929824561, mode=row)\n","      )\n","      (3): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.03859649122807018, mode=row)\n","      )\n","      (4): FusedMBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","            (1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.04210526315789474, mode=row)\n","      )\n","    )\n","    (4): Sequential(\n","      (0): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(80, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=320, bias=False)\n","            (1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(320, 20, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(20, 320, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(320, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.0456140350877193, mode=row)\n","      )\n","      (1): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.04912280701754387, mode=row)\n","      )\n","      (2): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.05263157894736842, mode=row)\n","      )\n","      (3): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.056140350877192984, mode=row)\n","      )\n","      (4): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.05964912280701755, mode=row)\n","      )\n","      (5): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.06315789473684211, mode=row)\n","      )\n","      (6): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)\n","            (1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.06666666666666667, mode=row)\n","      )\n","    )\n","    (5): Sequential(\n","      (0): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n","            (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(960, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.07017543859649122, mode=row)\n","      )\n","      (1): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.0736842105263158, mode=row)\n","      )\n","      (2): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.07719298245614035, mode=row)\n","      )\n","      (3): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.08070175438596493, mode=row)\n","      )\n","      (4): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.08421052631578949, mode=row)\n","      )\n","      (5): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.08771929824561403, mode=row)\n","      )\n","      (6): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.0912280701754386, mode=row)\n","      )\n","      (7): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.09473684210526316, mode=row)\n","      )\n","      (8): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.09824561403508773, mode=row)\n","      )\n","      (9): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.10175438596491229, mode=row)\n","      )\n","      (10): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.10526315789473684, mode=row)\n","      )\n","      (11): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.10877192982456141, mode=row)\n","      )\n","      (12): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.11228070175438597, mode=row)\n","      )\n","      (13): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.11578947368421054, mode=row)\n","      )\n","    )\n","    (6): Sequential(\n","      (0): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=1056, bias=False)\n","            (1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1056, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.1192982456140351, mode=row)\n","      )\n","      (1): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.12280701754385964, mode=row)\n","      )\n","      (2): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.12631578947368421, mode=row)\n","      )\n","      (3): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.1298245614035088, mode=row)\n","      )\n","      (4): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.13333333333333333, mode=row)\n","      )\n","      (5): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.1368421052631579, mode=row)\n","      )\n","      (6): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.14035087719298245, mode=row)\n","      )\n","      (7): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.14385964912280705, mode=row)\n","      )\n","      (8): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.1473684210526316, mode=row)\n","      )\n","      (9): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.15087719298245614, mode=row)\n","      )\n","      (10): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.1543859649122807, mode=row)\n","      )\n","      (11): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.15789473684210525, mode=row)\n","      )\n","      (12): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.16140350877192985, mode=row)\n","      )\n","      (13): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.1649122807017544, mode=row)\n","      )\n","      (14): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.16842105263157897, mode=row)\n","      )\n","      (15): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.17192982456140352, mode=row)\n","      )\n","      (16): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.17543859649122806, mode=row)\n","      )\n","      (17): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.17894736842105266, mode=row)\n","      )\n","    )\n","    (7): Sequential(\n","      (0): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)\n","            (1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(1824, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.1824561403508772, mode=row)\n","      )\n","      (1): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.18596491228070178, mode=row)\n","      )\n","      (2): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.18947368421052632, mode=row)\n","      )\n","      (3): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.19298245614035087, mode=row)\n","      )\n","      (4): MBConv(\n","        (block): Sequential(\n","          (0): Conv2dNormActivation(\n","            (0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (1): Conv2dNormActivation(\n","            (0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)\n","            (1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","            (2): SiLU(inplace=True)\n","          )\n","          (2): SqueezeExcitation(\n","            (avgpool): AdaptiveAvgPool2d(output_size=1)\n","            (fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))\n","            (fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))\n","            (activation): SiLU(inplace=True)\n","            (scale_activation): Sigmoid()\n","          )\n","          (3): Conv2dNormActivation(\n","            (0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","            (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","          )\n","        )\n","        (stochastic_depth): StochasticDepth(p=0.19649122807017547, mode=row)\n","      )\n","    )\n","    (8): Conv2dNormActivation(\n","      (0): Conv2d(512, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (1): BatchNorm2d(1280, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n","      (2): SiLU(inplace=True)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=1)\n","  (classifier): Sequential(\n","    (0): Dropout(p=0.3, inplace=True)\n","    (1): Linear(in_features=1280, out_features=2, bias=True)\n","  )\n",")"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Load EfficientNet V2 model with the updated 'weights' parameter\n","model = efficientnet_v2_m(weights=\"IMAGENET1K_V1\")\n","model.classifier[1] = nn.Linear(model.classifier[1].in_features, 2)  # Change output to 2 (binary classification)\n","\n","# Define optimizer\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","\n","# Set device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model.to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5680502,"status":"ok","timestamp":1732669867022,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"bd-hNl9EvG4x","outputId":"add62e47-c6b8-4924-ab4f-19b28a0bf544"},"outputs":[{"name":"stdout","output_type":"stream","text":["Default Training - Epoch 1, Loss: 0.1849407634912424\n","Default Training - Epoch 2, Loss: 0.13118009195422214\n","Default Training - Epoch 3, Loss: 0.10301178355054136\n","Default Training - Epoch 4, Loss: 0.07748161371694319\n","Default Training - Epoch 5, Loss: 0.056195093190242414\n","Elapsed time: 5679.92 seconds\n"]}],"source":["default_training(train_loader, val_loader, model, criterion, optimizer, device, num_epochs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Q6MroNQSwn72"},"outputs":[],"source":["torch.save(model.state_dict(), '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/EfficientNetV2M.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":76919,"status":"ok","timestamp":1732670021240,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"nRVoxT8A1eeR","outputId":"9e662a4e-a0ce-4433-bed9-f0ba06e718ba"},"outputs":[{"name":"stdout","output_type":"stream","text":["Validation Loss: 0.3265\n","Accuracy: 90.48%\n","F1 Score: 0.9118\n"]}],"source":["model.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/EfficientNetV2M.pth', weights_only=True))\n","\n","results = evaluate_model(model, val_loader, criterion, device)\n","print(f\"Validation Loss: {results['validation_loss']:.4f}\")\n","print(f\"Accuracy: {results['accuracy']:.2f}%\")\n","print(f\"F1 Score: {results['f1_score']:.4f}\")"]},{"cell_type":"markdown","metadata":{"id":"W9sHAq3U-HD5"},"source":["### Getting back all the metrics"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s4IS9sXP-PDj"},"outputs":[],"source":["def get_metrics(model_path, model_name, train_loader, val_loader, criterion):\n","    \"\"\"\n","    Loads a model based on the model_name, sets it up for binary classification,\n","    loads saved model weights, and computes training and validation metrics.\n","\n","    Args:\n","        model_path (str): Path to the saved model weights.\n","        model_name (str): Name of the model architecture to load.\n","        train_loader (DataLoader): DataLoader for training data.\n","        val_loader (DataLoader): DataLoader for validation data.\n","        criterion (nn.Module): Loss function.\n","\n","    Returns:\n","        None: The function prints out the training and validation results.\n","    \"\"\"\n","\n","    # Check if the provided model_name exists in torchvision.models\n","    if hasattr(models, model_name):\n","\n","        # Use getattr to get the model function dynamically from torchvision.models\n","        model_fn = getattr(models, model_name)\n","\n","        model = model_fn(weights=\"IMAGENET1K_V1\")\n","\n","        # Load the model with pre-trained weights\n","        if model_name == \"efficientnet_v2_m\":\n","            model.classifier[1] = nn.Linear(model.classifier[1].in_features, 2)  # Adjust the classifier layer\n","        elif model_name == \"inception_v3\":\n","            model.AuxLogits = nn.Sequential()  # Remove auxiliary classifier\n","            model.fc = nn.Linear(model.fc.in_features, 2)  # Adjust the final fully connected layer\n","        elif model_name in [\"mobilenet_v2\", \"mobilenet_v3_small\", \"mobilenet_v3_large\"]:\n","            model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, 2)  # Adjust the classifier layer\n","        else:\n","            model.fc = nn.Linear(model.fc.in_features, 2)  # Adjust the final fully connected layer for binary classification\n","\n","        # Load the model weights from the provided model_path\n","        model.load_state_dict(torch.load(model_path, weights_only=True))\n","\n","        # Set the model to evaluation mode\n","        model.eval()\n","\n","        # Move the model to the available device (CPU or GPU)\n","        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","        model.to(device)\n","\n","        # Evaluate the model on training and validation data\n","        train_results = evaluate_model(model, train_loader, criterion, device)\n","        train_loss = train_results['validation_loss']\n","        train_f1 = train_results['f1_score']\n","\n","        # Evaluate the model on validation data and print the results\n","        val_results = evaluate_model(model, val_loader, criterion, device)\n","        val_loss = val_results['validation_loss']\n","        val_f1 = val_results['f1_score']\n","\n","        print(f\"\\nMetrics for {model_name}:\")\n","        print(f\"Train Loss: {train_loss:.4f}, Train F1: {train_f1:.4f}, Val Loss: {val_loss:.4f}, Val F1: {val_f1:.4f}\")\n","\n","    else:\n","        # Raise an error if the provided model_name is not recognized\n","        raise ValueError(f\"Model name '{model_name}' is not recognized. Please provide a valid model name.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3970111,"status":"ok","timestamp":1732826458597,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"BFISOD_DAhsq","outputId":"a86f3e4f-9c2e-4511-8509-1c40628d6232"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Metrics for efficientnet_v2_m:\n","Train Loss: 0.0288, Train F1: 0.9892, Val Loss: 0.3265, Val F1: 0.9118\n","\n","Metrics for googlenet:\n","Train Loss: 0.1238, Train F1: 0.9603, Val Loss: 0.2933, Val F1: 0.9165\n","\n","Metrics for googlenet:\n","Train Loss: 0.1056, Train F1: 0.9658, Val Loss: 0.3340, Val F1: 0.8931\n","\n","Metrics for googlenet:\n","Train Loss: 0.1549, Train F1: 0.9432, Val Loss: 0.2226, Val F1: 0.9238\n","\n","Metrics for inception_v3:\n","Train Loss: 0.1973, Train F1: 0.9147, Val Loss: 0.2666, Val F1: 0.8812\n","\n","Metrics for inception_v3:\n","Train Loss: 0.1479, Train F1: 0.9484, Val Loss: 0.2261, Val F1: 0.9194\n","\n","Metrics for mobilenet_v2:\n","Train Loss: 0.1461, Train F1: 0.9496, Val Loss: 0.2512, Val F1: 0.9207\n","\n","Metrics for mobilenet_v2:\n","Train Loss: 0.0936, Train F1: 0.9668, Val Loss: 0.2699, Val F1: 0.9096\n","\n","Metrics for mobilenet_v2:\n","Train Loss: 0.0753, Train F1: 0.9735, Val Loss: 0.2449, Val F1: 0.9167\n"]},{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_large-8738ca79.pth\n","100%|██████████| 21.1M/21.1M [00:00<00:00, 126MB/s] \n"]},{"name":"stdout","output_type":"stream","text":["\n","Metrics for mobilenet_v3_large:\n","Train Loss: 0.2603, Train F1: 0.9386, Val Loss: 0.8269, Val F1: 0.8703\n","\n","Metrics for mobilenet_v3_large:\n","Train Loss: 0.2603, Train F1: 0.9386, Val Loss: 0.8269, Val F1: 0.8703\n","\n","Metrics for mobilenet_v3_small:\n","Train Loss: 0.0975, Train F1: 0.9636, Val Loss: 0.4319, Val F1: 0.8916\n","\n","Metrics for mobilenet_v3_small:\n","Train Loss: 0.0590, Train F1: 0.9758, Val Loss: 0.2751, Val F1: 0.9277\n","\n","Metrics for mobilenet_v3_small:\n","Train Loss: 0.1822, Train F1: 0.9476, Val Loss: 0.5542, Val F1: 0.8800\n","\n","Metrics for resnet18:\n","Train Loss: 0.2424, Train F1: 0.9296, Val Loss: 0.2630, Val F1: 0.9139\n","\n","Metrics for resnet18:\n","Train Loss: 0.1162, Train F1: 0.9626, Val Loss: 0.2653, Val F1: 0.9079\n","\n","Metrics for resnet18:\n","Train Loss: 0.0769, Train F1: 0.9737, Val Loss: 0.2525, Val F1: 0.9117\n"]},{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n","100%|██████████| 97.8M/97.8M [00:00<00:00, 163MB/s]\n"]},{"name":"stdout","output_type":"stream","text":["\n","Metrics for resnet50:\n","Train Loss: 0.1599, Train F1: 0.9395, Val Loss: 0.2696, Val F1: 0.8973\n"]}],"source":["# List of model paths and corresponding model names\n","model_paths = [\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/EfficientNetV2M.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/Googlenet.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/Googlenet_2ndRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/Googlenet_3rdRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/InceptionV3.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/InceptionV3_2ndRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV2.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV2_2ndRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV2_3rdRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3L.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3L_2ndRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3S.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3S_2ndRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/MobileNetV3S_3rdRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet18.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet18_2ndRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet18_3rdRun.pth',\n","    '/content/drive/MyDrive/ML Project 2 :)/Model_weights/Step_1_Model_archi/resnet50.pth'\n","]\n","\n","model_names = [\n","    'efficientnet_v2_m',\n","    'googlenet',\n","    'googlenet',\n","    'googlenet',\n","    'inception_v3',\n","    'inception_v3',\n","    'mobilenet_v2',\n","    'mobilenet_v2',\n","    'mobilenet_v2',\n","    'mobilenet_v3_large',\n","    'mobilenet_v3_large',\n","    'mobilenet_v3_small',\n","    'mobilenet_v3_small',\n","    'mobilenet_v3_small',\n","    'resnet18',\n","    'resnet18',\n","    'resnet18',\n","    'resnet50'\n","]\n","\n","# Assuming you have the train_loader, val_loader, and criterion set up:\n","for model_path, model_name in zip(model_paths, model_names):\n","    get_metrics(model_path, model_name, train_loader, val_loader, criterion)"]},{"cell_type":"markdown","metadata":{"id":"9BtXaMh5sVyj"},"source":["### Summary table"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":207,"status":"ok","timestamp":1732830472831,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"7CHTzRbYqWBt","outputId":"7d86d295-02d0-41a0-d629-f2b7e678fdf5"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"df_sorted\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"loss (train)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0580625524757567,\n        \"min\": 0.0288,\n        \"max\": 0.2295,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.105,\n          0.0288,\n          0.1726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-score % (train)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8458171825586018,\n        \"min\": 93.16,\n        \"max\": 98.92,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          96.33,\n          98.92,\n          93.16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loss (val)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14191536147094355,\n        \"min\": 0.2464,\n        \"max\": 0.6623,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2553,\n          0.3265,\n          0.2464\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-score % (val)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1876266849718142,\n        \"min\": 87.92,\n        \"max\": 91.57,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          91.57,\n          91.18,\n          90.03\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Running time [min]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 12,\n        \"max\": 95,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          23,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe","variable_name":"df_sorted"},"text/html":["\n","  <div id=\"df-72a615c7-192e-4e45-b88a-7dcfc5728853\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>loss (train)</th>\n","      <th>F1-score % (train)</th>\n","      <th>loss (val)</th>\n","      <th>F1-score % (val)</th>\n","      <th>Running time [min]</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Inception V3</th>\n","      <td>0.1726</td>\n","      <td>93.16</td>\n","      <td>0.2464</td>\n","      <td>90.03</td>\n","      <td>50</td>\n","    </tr>\n","    <tr>\n","      <th>MobileNet V2</th>\n","      <td>0.1050</td>\n","      <td>96.33</td>\n","      <td>0.2553</td>\n","      <td>91.57</td>\n","      <td>23</td>\n","    </tr>\n","    <tr>\n","      <th>ResNet18</th>\n","      <td>0.1452</td>\n","      <td>95.53</td>\n","      <td>0.2603</td>\n","      <td>91.12</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>ResNet50</th>\n","      <td>0.1559</td>\n","      <td>93.95</td>\n","      <td>0.2696</td>\n","      <td>89.73</td>\n","      <td>50</td>\n","    </tr>\n","    <tr>\n","      <th>GoogLeNet</th>\n","      <td>0.1281</td>\n","      <td>95.64</td>\n","      <td>0.2833</td>\n","      <td>91.11</td>\n","      <td>23</td>\n","    </tr>\n","    <tr>\n","      <th>EfficientNet V2-M</th>\n","      <td>0.0288</td>\n","      <td>98.92</td>\n","      <td>0.3265</td>\n","      <td>91.18</td>\n","      <td>95</td>\n","    </tr>\n","    <tr>\n","      <th>MobileNet V3-S</th>\n","      <td>0.1129</td>\n","      <td>96.23</td>\n","      <td>0.4204</td>\n","      <td>89.98</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>MobileNet V3-L</th>\n","      <td>0.2295</td>\n","      <td>93.75</td>\n","      <td>0.6623</td>\n","      <td>87.92</td>\n","      <td>20</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72a615c7-192e-4e45-b88a-7dcfc5728853')\"\n","            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This could be a sign of the last 3 models being too complex and overfitting. We will not consider them any further.\n","\n","Then, we notice that for the first 5 models of the table, not only are the losses similar but the F1-scores are also quite close to each other (less than 2% absolute difference), therefore considering lighter models that can be trained faster and help us in our following steps could be a smart choice. Lighter models for similar performance also mean that the right amount of computational cost and memory is needed (we don't want to use more weights than necessary).\n","\n","From this, we can keep 3 CNN architectures of interest : **MobileNet V2**, **ResNet18** and **GoogLeNet**, that use different strategies and that would therefore be interesting to explore further."]},{"cell_type":"markdown","metadata":{"id":"B7MuBc-EwTVy"},"source":["## More advanced model selection"]},{"cell_type":"markdown","source":["In this section, we want to perform a higher quality training to better compare the models.\n","\n","For the choice of optimizer that suits well the three models, we first looked at those that were used in the original papers: they were either Adam, or SGD with momentum. We then noticed that the PyTorch transfer learning tutorial used a learning rate lr=0.0001, so applied that to our models. However, the models started overfitting very strongly and fast, with lower validation losses. Then, we observed that using Adam with lr=0.001 showed better results, so the only added feature are 5 additional epochs, and some early stopping to prevent overfitting. The patience is set to 3, which is a nice amount considering the maximum number of epoch is 10. The training now also stores the best model (acc. to val. loss) accross all epochs.\n","\n","For now weight decay is applied, in order to conserve the pretrained weights.\n","\n","The ROC curves are plotted and the AUC are computed as an additional metrics to support our comparison. ROC curves are particularly powerful when comparing binary classifiers."],"metadata":{"id":"D6ujOb6CtsCN"}},{"cell_type":"markdown","metadata":{"id":"6Isuxd6eweRb"},"source":["### Training the models"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qhYsx1WOwgYr"},"outputs":[],"source":["import os\n","import torch\n","\n","def train_and_plot(train_loader, val_loader, model_fn, criterion, optimizer_fn, device, num_epochs, n_trials, patience, save_dir):\n","    \"\"\"\n","    Trains a model n times, saves the model after each trial, and plots the training and validation loss per epoch.\n","    Implements early stopping.\n","\n","    Args:\n","        train_loader (DataLoader): DataLoader for training data.\n","        val_loader (DataLoader): DataLoader for validation data.\n","        model_fn (function): A callable that returns a new model instance initialized with pre-trained weights.\n","        criterion (torch.nn.Module): Loss function.\n","        optimizer_fn (function): Function to create a new optimizer instance for each trial.\n","        device (torch.device): Device to use (CPU or GPU).\n","        num_epochs (int): Number of epochs per training.\n","        n_trials (int): Number of training repetitions.\n","        save_dir (str): Directory where the model for each trial will be saved.\n","        patience (int): Number of epochs to wait for validation loss improvement before stopping early.\n","    \"\"\"\n","    # Ensure the save directory exists\n","    os.makedirs(save_dir, exist_ok=True)\n","\n","    sample_model = model_fn()\n","    model_name = type(sample_model).__name__\n","\n","    plt.figure(figsize=(10, 6))\n","\n","    # To store all losses for averaging\n","    all_train_losses = []\n","    all_val_losses = []\n","\n","    for trial in range(n_trials):\n","        # Reinitialize the model with ImageNet weights\n","        model = model_fn().to(device)\n","\n","        # Create a new optimizer instance for this trial\n","        optimizer = optimizer_fn(model.parameters())\n","\n","        train_losses = []\n","        val_losses = []\n","\n","        best_val_loss = float('inf')  # Initialize to a large value\n","        patience_counter = 0  # Counter to track early stopping patience\n","\n","        for epoch in range(num_epochs):\n","            # Training phase\n","            model.train()\n","            running_loss = 0.0\n","            for images, labels in train_loader:\n","                images, labels = images.to(device), labels.to(device)\n","\n","                optimizer.zero_grad()\n","                outputs = model(images)\n","                loss = criterion(outputs, labels)\n","                loss.backward()\n","                optimizer.step()\n","\n","                running_loss += loss.item()\n","            train_loss = running_loss / len(train_loader)\n","            train_losses.append(train_loss)\n","\n","            # Validation phase\n","            model.eval()\n","            val_running_loss = 0.0\n","            with torch.no_grad():\n","                for images, labels in val_loader:\n","                    images, labels = images.to(device), labels.to(device)\n","                    outputs = model(images)\n","                    loss = criterion(outputs, labels)\n","                    val_running_loss += loss.item()\n","            val_loss = val_running_loss / len(val_loader)\n","            val_losses.append(val_loss)\n","\n","            # Print loss for the current epoch\n","            print(f\"Trial {trial + 1}, Epoch {epoch + 1}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}\")\n","\n","            # Check for early stopping\n","            if val_loss < best_val_loss:\n","                best_val_loss = val_loss\n","                patience_counter = 0  # Reset patience counter\n","                # Optionally save the best model for this trial\n","                trial_best_model_path = os.path.join(save_dir, f\"{model_name}_trial_{trial + 1}_best.pth\")\n","                torch.save(model.state_dict(), trial_best_model_path)\n","                print(f\"New best model for Trial {trial + 1} saved to {trial_best_model_path}\")\n","            else:\n","                patience_counter += 1\n","                if patience_counter >= patience:\n","                    print(f\"Early stopping triggered for Trial {trial + 1} at Epoch {epoch + 1}\")\n","                    break\n","\n","\n","        # Save the final model for the trial\n","        trial_model_path = os.path.join(save_dir, f\"{model_name}_trial_{trial + 1}.pth\")\n","        torch.save(model.state_dict(), trial_model_path)\n","        print(f\"Final model for Trial {trial + 1} saved to {trial_model_path}\")\n","\n","        # Store losses for averaging\n","        all_train_losses.append(train_losses)\n","        all_val_losses.append(val_losses)\n","\n","        # Plot training and validation losses for this trial\n","        plt.plot(range(1, len(train_losses) + 1), train_losses, color='blue', alpha=0.3, label='Training Loss' if trial == 0 else \"\")\n","        plt.plot(range(1, len(val_losses) + 1), val_losses, color='orange', alpha=0.3, label='Validation Loss' if trial == 0 else \"\")\n","\n","    # Calculate average losses per epoch\n","    avg_train_losses = [sum(epoch_losses) / n_trials for epoch_losses in zip(*all_train_losses)]\n","    avg_val_losses = [sum(epoch_losses) / n_trials for epoch_losses in zip(*all_val_losses)]\n","\n","    # Plot average lines\n","    plt.plot(range(1, len(avg_train_losses) + 1), avg_train_losses, color='blue', linewidth=2, label='Avg Training Loss')\n","    plt.plot(range(1, len(avg_val_losses) + 1), avg_val_losses, color='orange', linewidth=2, label='Avg Validation Loss')\n","\n","    # Finalize plot\n","    plt.xticks(range(1, num_epochs + 1))  # Ensure x-axis shows integer epochs\n","    plt.xlabel(\"Epoch\")\n","    plt.ylabel(\"Loss\")\n","    plt.title(\"Training and Validation Loss Across Trials\")\n","    plt.legend(loc=\"upper right\")\n","    plt.grid()\n","    plt.show()\n"]},{"cell_type":"markdown","metadata":{"id":"k2TGg57uwks4"},"source":["#### Model 1 : MobileNetV2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":966},"executionInfo":{"elapsed":4303971,"status":"ok","timestamp":1734137779540,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"9ZwIMwHOwng9","outputId":"e8e871e9-e522-49c0-b76e-4ef4167a8155"},"outputs":[{"name":"stdout","output_type":"stream","text":["New best model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_1_best.pth\n","Trial 1, Epoch 1, Train Loss: 0.1994, Val Loss: 0.2210\n","Trial 1, Epoch 2, Train Loss: 0.1538, Val Loss: 0.2529\n","Trial 1, Epoch 3, Train Loss: 0.1336, Val Loss: 0.2343\n","Early stopping triggered for Trial 1 at Epoch 4\n","Final model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_1.pth\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_2_best.pth\n","Trial 2, Epoch 1, Train Loss: 0.1964, Val Loss: 0.2730\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_2_best.pth\n","Trial 2, Epoch 2, Train Loss: 0.1572, Val Loss: 0.2460\n","Trial 2, Epoch 3, Train Loss: 0.1342, Val Loss: 0.2463\n","Trial 2, Epoch 4, Train Loss: 0.1161, Val Loss: 0.2893\n","Early stopping triggered for Trial 2 at Epoch 5\n","Final model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_2.pth\n","New best model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_3_best.pth\n","Trial 3, Epoch 1, Train Loss: 0.1930, Val Loss: 0.3597\n","New best model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_3_best.pth\n","Trial 3, Epoch 2, Train Loss: 0.1478, Val Loss: 0.2144\n","Trial 3, Epoch 3, Train Loss: 0.1286, Val Loss: 0.2296\n","Trial 3, Epoch 4, Train Loss: 0.1113, Val Loss: 0.2789\n","Early stopping triggered for Trial 3 at Epoch 5\n","Final model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_3.pth\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["def create_mobilenetv2():\n","    model = mobilenet_v2(weights=\"IMAGENET1K_V1\")\n","    model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, 2)\n","    return model\n","\n","# Define optimizer function\n","def optimizer_fn(params):\n","    return torch.optim.Adam(params, lr=1e-3)\n","\n","# Train and plot\n","train_and_plot(train_loader, val_loader, create_mobilenetv2, criterion, optimizer_fn,\n","               device, num_epochs=10, n_trials=3, patience=3, save_dir='/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial')"]},{"cell_type":"markdown","metadata":{"id":"s5YZ38krwtrB"},"source":["#### Model 2 : ResNet18"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":5723743,"status":"ok","timestamp":1734143503276,"user":{"displayName":"natank sabak","userId":"16628741557264012685"},"user_tz":-60},"id":"nxeUeWrQwwpl","outputId":"4e419a42-7dcb-4753-83f9-93c69e30a42a"},"outputs":[{"name":"stdout","output_type":"stream","text":["New best model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_1_best.pth\n","Trial 1, Epoch 1, Train Loss: 0.2486, Val Loss: 0.4199\n","New best model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_1_best.pth\n","Trial 1, Epoch 2, Train Loss: 0.2034, Val Loss: 0.2864\n","New best model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_1_best.pth\n","Trial 1, Epoch 3, Train Loss: 0.1741, Val Loss: 0.2310\n","Trial 1, Epoch 4, Train Loss: 0.1574, Val Loss: 0.2376\n","Trial 1, Epoch 5, Train Loss: 0.1371, Val Loss: 0.2596\n","Early stopping triggered for Trial 1 at Epoch 6\n","Final model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_1.pth\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_2_best.pth\n","Trial 2, Epoch 1, Train Loss: 0.2371, Val Loss: 0.4613\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_2_best.pth\n","Trial 2, Epoch 2, Train Loss: 0.1813, Val Loss: 0.3503\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_2_best.pth\n","Trial 2, Epoch 3, Train Loss: 0.1531, Val Loss: 0.3071\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_2_best.pth\n","Trial 2, Epoch 4, Train Loss: 0.1263, Val Loss: 0.1884\n","Trial 2, Epoch 5, Train Loss: 0.0952, Val Loss: 0.4430\n","Trial 2, Epoch 6, Train Loss: 0.0655, Val Loss: 0.2430\n","Early stopping triggered for Trial 2 at Epoch 7\n","Final model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_2.pth\n","New best model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_3_best.pth\n","Trial 3, Epoch 1, Train Loss: 0.2322, Val Loss: 0.3669\n","New best model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_3_best.pth\n","Trial 3, Epoch 2, Train Loss: 0.1844, Val Loss: 0.2990\n","Trial 3, Epoch 3, Train Loss: 0.1603, Val Loss: 0.3175\n","Trial 3, Epoch 4, Train Loss: 0.1344, Val Loss: 0.3319\n","New best model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_3_best.pth\n","Trial 3, Epoch 5, Train Loss: 0.1066, Val Loss: 0.2495\n","Trial 3, Epoch 6, Train Loss: 0.0758, Val Loss: 0.2515\n","Trial 3, Epoch 7, Train Loss: 0.0523, Val Loss: 0.2735\n","Early stopping triggered for Trial 3 at Epoch 8\n","Final model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_3.pth\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["def create_resnet18():\n","    model = resnet18(weights=\"IMAGENET1K_V1\")\n","    model.fc = nn.Linear(model.fc.in_features, 2)\n","    return model\n","\n","# Define optimizer function\n","def optimizer_fn(params):\n","    return torch.optim.Adam(params, lr=1e-3)\n","\n","# Train and plot\n","train_and_plot(train_loader, val_loader, create_resnet18, criterion, optimizer_fn,\n","               device, num_epochs=10, n_trials=3,  patience=3, save_dir='/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial')"]},{"cell_type":"markdown","metadata":{"id":"ph2ZN8CNwyfz"},"source":["#### Model 3 : GoogLeNet"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"kGeiJnSBw4C_","outputId":"d722e4b3-11f2-4e03-b37b-8f02d6009952"},"outputs":[{"name":"stderr","output_type":"stream","text":["Downloading: \"https://download.pytorch.org/models/googlenet-1378be20.pth\" to /root/.cache/torch/hub/checkpoints/googlenet-1378be20.pth\n","100%|██████████| 49.7M/49.7M [00:00<00:00, 218MB/s]\n"]},{"name":"stdout","output_type":"stream","text":["New best model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_1_best.pth\n","Trial 1, Epoch 1, Train Loss: 0.2069, Val Loss: 0.2582\n","New best model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_1_best.pth\n","Trial 1, Epoch 2, Train Loss: 0.1591, Val Loss: 0.2312\n","Trial 1, Epoch 3, Train Loss: 0.1368, Val Loss: 0.2324\n","Trial 1, Epoch 4, Train Loss: 0.1136, Val Loss: 0.2884\n","New best model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_1_best.pth\n","Trial 1, Epoch 5, Train Loss: 0.0937, Val Loss: 0.2245\n","Trial 1, Epoch 6, Train Loss: 0.0778, Val Loss: 0.2461\n","Trial 1, Epoch 7, Train Loss: 0.0603, Val Loss: 0.2409\n","Early stopping triggered for Trial 1 at Epoch 8\n","Final model for Trial 1 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_1.pth\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_2_best.pth\n","Trial 2, Epoch 1, Train Loss: 0.2117, Val Loss: 0.2512\n","Trial 2, Epoch 2, Train Loss: 0.1614, Val Loss: 0.4359\n","New best model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_2_best.pth\n","Trial 2, Epoch 3, Train Loss: 0.1379, Val Loss: 0.1945\n","Trial 2, Epoch 4, Train Loss: 0.1232, Val Loss: 0.2252\n","Trial 2, Epoch 5, Train Loss: 0.1007, Val Loss: 0.2224\n","Early stopping triggered for Trial 2 at Epoch 6\n","Final model for Trial 2 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_2.pth\n","New best model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_3_best.pth\n","Trial 3, Epoch 1, Train Loss: 0.2064, Val Loss: 0.2164\n","Trial 3, Epoch 2, Train Loss: 0.1620, Val Loss: 0.3057\n","New best model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_3_best.pth\n","Trial 3, Epoch 3, Train Loss: 0.1394, Val Loss: 0.2038\n","Trial 3, Epoch 4, Train Loss: 0.1141, Val Loss: 0.2657\n","Trial 3, Epoch 5, Train Loss: 0.0977, Val Loss: 0.2878\n","Early stopping triggered for Trial 3 at Epoch 6\n","Final model for Trial 3 saved to /content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_3.pth\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["def create_googlenet():\n","    model = googlenet(weights=\"IMAGENET1K_V1\")\n","    model.fc = nn.Linear(model.fc.in_features, 2)\n","    return model\n","\n","# Define optimizer function\n","def optimizer_fn(params):\n","    return torch.optim.Adam(params, lr=1e-3)\n","# Train and plot\n","train_and_plot(train_loader, val_loader, create_googlenet, criterion, optimizer_fn,\n","               device, num_epochs=10, n_trials=3,  patience=3, save_dir='/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial')"]},{"cell_type":"markdown","metadata":{"id":"KL1X637Uy31v"},"source":["### ROC curves and AUC"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X_AX3Xlqy6D-"},"outputs":[],"source":["from sklearn.metrics import roc_curve, auc\n","\n","def plot_average_roc(models, val_loader, device, labels):\n","    \"\"\"\n","    Plots the ROC curve for multiple models and their average.\n","\n","    Args:\n","        models (list of torch.nn.Module): List of trained models.\n","        val_loader (DataLoader): DataLoader for the validation set.\n","        device (torch.device): Device to use (CPU or GPU).\n","        labels (list of str): Names of the models for legend labeling.\n","    \"\"\"\n","    all_fpr = np.linspace(0, 1, 100)  # Common FPR axis for interpolation\n","    tpr_list = []  # Store TPR values for each model's interpolated curve\n","    auc_list = []  # Store AUC values for each model\n","\n","    plt.figure(figsize=(10, 8))\n","\n","    for i, model in enumerate(models):\n","        model.eval()\n","        true_labels = []\n","        pred_probs = []\n","\n","        # Disable gradient computation during evaluation\n","        with torch.no_grad():\n","            for images, labels in val_loader:\n","                images, labels = images.to(device), labels.to(device)\n","\n","                # Forward pass\n","                outputs = model(images)\n","                probabilities = outputs[:, 1]  # Use logits for positive class\n","\n","                # Append true labels and predicted probabilities\n","                true_labels.extend(labels.cpu().numpy())\n","                pred_probs.extend(probabilities.cpu().numpy())\n","\n","        # Calculate ROC curve metrics\n","        fpr, tpr, _ = roc_curve(true_labels, pred_probs)\n","        roc_auc = auc(fpr, tpr)\n","        auc_list.append(roc_auc)\n","\n","        # Interpolate TPR to common FPR axis\n","        interpolated_tpr = np.interp(all_fpr, fpr, tpr)\n","        tpr_list.append(interpolated_tpr)\n","\n","        # Plot individual model's ROC curve in light grey\n","        plt.plot(fpr, tpr, color='lightgrey', lw=1, label=f\"{labels[i]} (AUC = {roc_auc:.2f})\")\n","\n","    # Compute the average TPR\n","    mean_tpr = np.mean(tpr_list, axis=0)\n","    mean_auc = auc(all_fpr, mean_tpr)\n","\n","    # Plot the average ROC curve in blue\n","    plt.plot(all_fpr, mean_tpr, color='blue', lw=2, label=f\"Average (AUC = {mean_auc:.2f})\")\n","\n","    # Add baseline\n","    plt.plot([0, 1], [0, 1], color='red', linestyle='--', label=\"Random Classifier (AUC = 0.5)\")\n","\n","    # Add labels, title, and legend\n","    plt.xlabel(\"False Positive Rate\")\n","    plt.ylabel(\"True Positive Rate\")\n","    plt.title(\"Average ROC Curve Across Models\")\n","    plt.legend(loc=\"lower right\")\n","    plt.grid()\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nbdUTabsy_QJ"},"outputs":[],"source":["# Define loss\n","criterion = nn.CrossEntropyLoss()\n","\n","# Create dataloaders\n","train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n","val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n","\n","# Device\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"]},{"cell_type":"markdown","metadata":{"id":"D3zajxRnzHd-"},"source":["#### Model 1 : MobileNetV2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"LCG1MjeczLVd","outputId":"4c478ce7-2f1b-4273-c8a0-3bb2d3e3b785"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["mobilenet1 = mobilenet_v2(weights=\"IMAGENET1K_V1\")\n","mobilenet1.classifier[-1] = nn.Linear(mobilenet1.classifier[-1].in_features, 2)\n","mobilenet1.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_1_best.pth', weights_only=True))\n","\n","mobilenet2 = mobilenet_v2(weights=\"IMAGENET1K_V1\")\n","mobilenet2.classifier[-1] = nn.Linear(mobilenet2.classifier[-1].in_features, 2)\n","mobilenet2.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_2_best.pth', weights_only=True))\n","\n","mobilenet3 = mobilenet_v2(weights=\"IMAGENET1K_V1\")\n","mobilenet3.classifier[-1] = nn.Linear(mobilenet3.classifier[-1].in_features, 2)\n","mobilenet3.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/MobileNetV2_trial_3_best.pth', weights_only=True))\n","\n","\n","my_models = [mobilenet1, mobilenet2, mobilenet3]\n","for model in my_models:\n","    model = model.to(device)\n","labels = [\"mobilenet1\", \"mobilenet2\", \"mobilenet3\"]\n","\n","plot_average_roc(my_models, val_loader, device, labels)"]},{"cell_type":"markdown","metadata":{"id":"U0e-dmBczZDO"},"source":["#### Model 2 : ResNet18"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"H4LEO0B6zcAF","outputId":"62b2b789-e09d-4716-b078-6f37d397e97f"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["resnet1 = models.resnet18(weights=\"IMAGENET1K_V1\")\n","resnet1.fc = nn.Linear(resnet1.fc.in_features, 2)\n","resnet1.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_1_best.pth', weights_only=True))\n","\n","resnet2 = models.resnet18(weights=\"IMAGENET1K_V1\")\n","resnet2.fc = nn.Linear(resnet2.fc.in_features, 2)\n","resnet2.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_2_best.pth', weights_only=True))\n","\n","resnet3 = models.resnet18(weights=\"IMAGENET1K_V1\")\n","resnet3.fc = nn.Linear(resnet3.fc.in_features, 2)\n","resnet3.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/ResNet_trial_3_best.pth', weights_only=True))\n","\n","my_models = [resnet1, resnet2, resnet3]\n","for model in my_models:\n","    model = model.to(device)\n","labels = [\"resnet1\", \"resnet2\", \"resnet3\"]\n","\n","plot_average_roc(my_models, val_loader, device, labels)"]},{"cell_type":"markdown","metadata":{"id":"dO4eN7dTzlxC"},"source":["#### Model 3 : GoogLeNet"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":753},"id":"OcQmegM6zoRb","executionInfo":{"status":"ok","timestamp":1734170416356,"user_tz":-60,"elapsed":114309,"user":{"displayName":"natank sabak","userId":"16628741557264012685"}},"outputId":"7721295a-2177-471a-c803-02b2e7c728cc"},"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading: \"https://download.pytorch.org/models/googlenet-1378be20.pth\" to /root/.cache/torch/hub/checkpoints/googlenet-1378be20.pth\n","100%|██████████| 49.7M/49.7M [00:00<00:00, 133MB/s]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["googlenet1 = models.googlenet(weights=\"IMAGENET1K_V1\")\n","googlenet1.fc = nn.Linear(googlenet1.fc.in_features, 2)\n","googlenet1.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_1_best.pth', weights_only=True))\n","\n","googlenet2 = models.googlenet(weights=\"IMAGENET1K_V1\")\n","googlenet2.fc = nn.Linear(googlenet2.fc.in_features, 2)\n","googlenet2.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_2_best.pth', weights_only=True))\n","\n","googlenet3 = models.googlenet(weights=\"IMAGENET1K_V1\")\n","googlenet3.fc = nn.Linear(googlenet3.fc.in_features, 2)\n","googlenet3.load_state_dict(torch.load('/content/drive/MyDrive/ML Project 2 :)/Model_weights/Advanced_model_selection/second_trial/GoogLeNet_trial_3_best.pth', weights_only=True))\n","\n","my_models = [googlenet1, googlenet2, googlenet3]\n","for model in my_models:\n","    model = model.to(device)\n","labels = [\"googlenet1\", \"googlenet2\", \"googlenet3\"]\n","\n","plot_average_roc(my_models, val_loader, device, labels)"]},{"cell_type":"markdown","source":["## Model selection : conclusion"],"metadata":{"id":"S2IkRn5BrO1A"}},{"cell_type":"markdown","source":["Considering the binary cross-entropy losses over the validation set, and taking the average over the three runs, GoogLeNet has the lowest loss (0.2076) followed by ResNet18 (0.2230) and MobileNetV2 (0.2271).\n","\n","Looking at the AUCs, GoogLeNet also comes first (0.91) tied with MobileNetV2 (0.91) and ResNet18 (0.89).\n","\n","We can also note that ResNet18 has the highest variance in results, as shown by the ROC curves. In addition, the training of MobileNetV2 is stopped the earliest and indicates higher overfitting.\n","\n","Therefore, the best option seems to be GoogLeNet."],"metadata":{"id":"2EgrJj_wrSU4"}}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["_0aHpLq4dUgg","H3MdtvYDnD5W","ZBba8Jh5fA2f","uoYDCXNVWFlc","DOQ1-0ZzphKc"],"gpuType":"T4","machine_shape":"hm","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}