Image Classification in Java Using Deep Learning

Image Recognition in JavImage classifier uses deep learning to learn from examples how to automatically put images into appropriate categories / folders. In this tutorial we’ll use example images of food to build classifier that is able to distinguish hot-dog from not-hot-dog images. Watch the funny video below from TV Show Sillicon Valley to get the basic idea.

Start by cloning this GitHub repo: https://github.com/deepnetts/hotdog-nothotdog

In order to run the example you need to download and install Deep Netts.

The following code loads images from a file system, and prepares them for neural network training.

ImageSet imageSet = new ImageSet(imageWidth, imageHeight, dataSetPath);
imageSet.setResizeStrategy(ImageResize.STRATCH);
imageSet.setInvertImages(true);
imageSet.zeroMean();

Convolutional neural networks are specialized for image recognition tasks, and the following code creates small convolutional network which is configured to perform binary image classification.

ConvolutionalNetwork convNet = ConvolutionalNetwork.builder()
                .addInputLayer(imageWidth, imageHeight, 3)
                .addConvolutionalLayer(3, Filter.ofSize(3))
                .addMaxPoolingLayer(2, 2)
                .addFullyConnectedLayer(32)
                .addOutputLayer(1, ActivationType.SIGMOID)
                .hiddenActivationFunction(ActivationType.LEAKY_RELU)
                .lossFunction(LossType.CROSS_ENTROPY)
                .build();

To train neural network using example/training images you need to run the backpropagation training algorithm using loaded image set using the following code:

BackpropagationTrainer trainer = convNet.getTrainer();
trainer.setStopError(0.03f)  
       .setLearningRate(0.01f)
       .setStopAccuracy(0.97f)
       .setStopEpochs(300);
trainer.train(imageSet);

To see how accurately neural network is able to recognize images perform the following test:

EvaluationMetrics testResults = convNet.test(imageSet);
 System.out.println(testResults); 

To save a trained neural network into file, to be able to use it in your app just call the save method and specify name of the file to save the network into:

// SAVE THE MODEL - save trained neural network to file
convNet.save("HotDogClassifier.dnet");

To use the trained image classifier in your app use the following code snippet:

// load image from file
BufferedImage image = ImageIO.read(new File("hotdog-dataset/hot_dog/7896.jpg")).getSubimage(0, 0, 64, 64);

// load a trained convolutional network
ConvolutionalNetwork convNet = ConvolutionalNetwork.load("HotDogClassifier.dnet", ConvolutionalNetwork.class);
        
// instantiate an image classifier using convolutional neural network
ImageClassifier<BufferedImage> imageClassifier = new ImageClassifierNetwork(convNet);
        
// classify image and get results
Map<String, Float> results = imageClassifier.classify(image); // result is a map with image labels as keys and coresponding probability