def predict(dataloader, model):

    # Turn off gradient calculation
    with torch.no_grad():

        img_lst = []; y_pred_lst = []; name_lst = []
        # Loop over data loader
        for image, name in dataloader:
            img_lst.append(image)
            image = image.to(device)

            # Get raw values from model
            output = model(image)

            # Derive prediction
            y_pred = output.argmax(1)
            y_pred_lst.append(y_pred.item())
            name_lst.append(name[0])

    return img_lst, y_pred_lst, name_lst