I’m currently working on training a MobileNet V3 Small model for object detection using PyTorch. I’ve successfully implemented the training loop, but I’m unsure how to write the validation loop correctly. I need help with writing a validation loop. Below is my current training loop:

# Set the loss function
criterion = nn.CrossEntropyLoss()

# Set the optimizer
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# Set the number of epochs
num_epochs = 10

# Set the batch size
batch_size = 32

# Set the number of iterations per epoch
num_iterations = len(dataset) // batch_size

# Training loop
for epoch in range(num_epochs):
    running_loss = 0.0
    running_accuracy = 0.0
    
    for i, (inputs, labels) in enumerate(dataloader):
        # Move inputs and labels to the device
        inputs = inputs.to(device)
        labels = labels.to(device)
        
        # Zero the gradients
        optimizer.zero_grad()
        
        # Forward pass
        outputs = model(inputs)
        
        # Compute the loss
        loss = criterion(outputs, labels)
        
        # Backward pass
        loss.backward()
        
        # Update the weights
        optimizer.step()
        
        # Compute the accuracy
        predictions = torch.argmax(outputs, dim=1)
        accuracy = torchmetrics.Accuracy()(predictions, labels)
        
        # Update the running loss and accuracy
        running_loss += loss.item()
        running_accuracy += accuracy.item()
        
        # Print the progress every 100 iterations
        if (i + 1) % 100 == 0:
            print(f"Epoch [{epoch+1}/{num_epochs}], Iteration [{i+1}/{num_iterations}], Loss: {running_loss/100:.4f}, Accuracy: {running_accuracy/100:.4f}")
            
            running_loss = 0.0
            running_accuracy = 0.0