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Recognizing Emergency Vehicles Using Pytorch and GPU | Realcode4you

Objective

In this task (T1-T3), you will be developing neural networks to recognize the images of emergencyvehicles (e.g., police cars, fire trucks, ambulances, etc.). Imagine a smart CCTV/traffic light that can automatically recognize emergencyvehicles and then adjust traffic lights/alarm road traffic accordingly. You will be given an image dataset with 681 images on emergencyvehicles and 965 on non-emergencyvehicles in A3-data folder.


Your Tasks

T1. Read and preprocess the data properly.

  • Upload the data to Google Drive and read them into PyTorch dataloader.

  • You should strategically separate 75% of the data into training while the rest 25% for validation. Here “strategically” means in both training and validation, the proportion of emergency data should be the same or very close.

  • You may copy some from Reference (4 ipynb, FMnist_Transfer and Fashion_MNIST_and_CNN) as attached while there is a need to add some lines/structures of codes that are not covered. The type for this task should be similar to the Reference.

T2. Develop a CNN-based neural network to recognize emergency vehicles.

  • Follow the structure shown in Figure 1 to write your network.

  • Train and validate your network using GPU. To save your time, set epoch<=10.

  • Draw a feasible line chart to monitor your train/validation loss.

  • Adjust/optimize your parameters and settings if the curve of validation loss is not nice.

T3. Develop a network to recognize the emergency vehicles based on transfer learning.

  • Use ResNet-50 (model = models.resnet50(pretrained=TRUE)) as your base model to establish the transfer learning. Modify and train the last fully connected layer of ResNet-50 and freeze other layers. Note that PyTorch has predefined ResNet-50 in torchvision.models, so you don’t need to develop its structure one more time.

  • Train and validate your transferred network using GPU. To save your time, set epoch<=10.

  • Draw a feasible line chart to monitor your train/validation loss.


Note:

1. Please output your code into ipynb

2. Try not to continuously open GPU > 12 hours. Google may block you for 24 hours if you do so.


Implementation

T1. Read and preprocess the data properly.

#draft solution (Missing 'no"?)
import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import ToTensor
from PIL import Image

# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

# Define dataset class for A3-data
class A3Dataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.images = []
        self.labels = []
        for label in os.listdir(root_dir):
            label_dir = os.path.join(root_dir, label)
            if os.path.isdir(label_dir):
                for filename in os.listdir(label_dir):
                    image = Image.open(os.path.join(label_dir, filename))
                    self.images.append(image)
                    self.labels.append(int(label == 'yes'))

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        image = self.images[idx]
        label = self.labels[idx]
        if self.transform:
            image = self.transform(image)
        return image, label

# Define transforms to convert PIL images to tensors
transform = ToTensor()

# Load data into A3Dataset
data_dir = '/content/drive/MyDrive/A3-data'
dataset = A3Dataset(data_dir, transform)

# Split dataset into training and validation sets
n_samples = len(dataset)
train_size = int(0.75 * n_samples)
val_size = n_samples - train_size
train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

# Create DataLoaders
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=True)



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