// Assume user uses a pre-trained model from model zoo, they just need to load it
    Criteria<Image, Classifications> criteria =
            Criteria.builder()
                    .optApplication(Application.CV.OBJECT_DETECTION) // find object detection model
                    .setTypes(Image.class, Classifications.class)    // define input and output
                    .optFilter("backbone", "resnet50")               // choose network architecture
                    .build();
    Image img = ImageFactory.getInstance().fromUrl("https://...");    // read image
    try (ZooModel<Image, Classifications> model = criteria.loadModel();
         Predictor<Image, Classifications> predictor = model.newPredictor()) {
        Classifications result = predictor.predict(img);
        // get the classification and probability
        ...
    }

    // Construct your neural network with built-in blocks
    Block block = new Mlp(28 * 28, 10, new int[] {128, 64});
    Model model = Model.newInstance("mlp"); // Create an empty model
    model.setBlock(block);                  // set neural network to model
    // Get training and validation dataset (MNIST dataset)
    Dataset trainingSet = new Mnist.Builder().setUsage(Usage.TRAIN) ... .build();
    Dataset validateSet = new Mnist.Builder().setUsage(Usage.TEST) ... .build();
    // Setup training configurations, such as Initializer, Optimizer, Loss ...
    TrainingConfig config = setupTrainingConfig();
    Trainer trainer = model.newTrainer(config);
    /*
     * Configure input shape based on dataset to initialize the trainer.
     * 1st axis is batch axis, we can use 1 for initialization.
     * MNIST is 28x28 grayscale image and pre processed into 28 * 28 NDArray.
     */
    trainer.initialize(new Shape(1, 28 * 28));
    EasyTrain.fit(trainer, epoch, trainingSet, validateSet);
    // Save the model
    model.save(modelDir, "mlp");
    // Close the resources
    trainer.close();
    model.close();