Rumeysakeskin/EuroSat-Satellite-CNN-and-ResNet

Classifying custom image datasets by creating Convolutional Neural Networks and Residual Networks from scratch with PyTorch

Classifying custom image datasets by creating Convolutional Neural Networks and Residual Networks from scratch with PyTorch

• Dataset

EuroSAT: Land Use and Land Cover Classification with Sentinel-2

• Create data and dataloaders

train_dataset = datasets.ImageFolder(os.path.join(DATASET_PATH, 'train'), data_transforms)
test_dataset = datasets.ImageFolder(os.path.join(DATASET_PATH, 'test'), data_transforms)

train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
test_loader = DataLoader(test_dataset, BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True)

• Create transformations

from torchvision import datasets, models, transforms
data_transforms = transforms.Compose([
        transforms.Resize(64),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

• Create models

# Create Convolutional Neural Network
model = CNN()

# Create Residual Network
model = ResNet(ResidualBlock, [2, 2, 2])

• Open and run train_cnn.ipynb and train_resnet.ipynb jupyter notebooks for train and evaluate models for Euro SAT dataset.

# Main loop
train_loss = []
train_accuracy = []
test_loss = []
test_accuracy = []
epochs = []

for epoch in range(1, NUM_EPOCHS+1):
    print(f'\n\nRunning epoch {epoch} of {NUM_EPOCHS}...\n')
    epochs.append(epoch)

    #-------------------------Train-------------------------
    
    #Reset these below variables to 0 at the begining of every epoch
    correct = 0
    iterations = 0
    iter_loss = 0.0
    
    model.train()  # Put the network into training mode
    
    for i, (inputs, labels) in enumerate(train_loader):
       
        if USE_CUDA:
            inputs = inputs.cuda()
            labels = labels.cuda()        
            
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        iter_loss += loss.item()  # Accumulate the loss
        optimizer.zero_grad() # Clear off the gradient in (w = w - gradient)
        loss.backward()   # Backpropagation 
        optimizer.step()  # Update the weights
        
        # Record the correct predictions for training data 
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == labels).sum()
        iterations += 1
        
    scheduler.step()
        
    # Record the training loss
    train_loss.append(iter_loss/iterations)
    # Record the training accuracy
    train_accuracy.append((100 * correct / len(train_dataset)))   
     
    #-------------------------Test--------------------------
    
    correct = 0
    iterations = 0
    testing_loss = 0.0
    
    model.eval()  # Put the network into evaluation mode
    
    for i, (inputs, labels) in enumerate(test_loader):

        if USE_CUDA:
            inputs = inputs.cuda()
            labels = labels.cuda()
        
        outputs = model(inputs)     
        loss = criterion(outputs, labels) # Calculate the loss
        testing_loss += loss.item()
        # Record the correct predictions for training data
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == labels).sum()
        
        iterations += 1

    # Record the Testing loss
    test_loss.append(testing_loss/iterations)
    # Record the Testing accuracy
    test_accuracy.append((100 * correct / len(test_dataset)))
   
    print(f'\nEpoch {epoch} validation results: Loss={test_loss[-1]} | Accuracy={test_accuracy[-1]}\n')