public class TinyYOLO
extends ZooModel

Tiny YOLO Reference: https://arxiv.org/pdf/1612.08242.pdf

ImageNet+VOC weights for this model are available and have been converted from https://pjreddie.com/darknet/yolo/ using https://github.com/allanzelener/YAD2K and the following code.

     String filename = "tiny-yolo-voc.h5";
     ComputationGraph graph = KerasModelImport.importKerasModelAndWeights(filename, false);
     INDArray priors = Nd4j.create(priorBoxes);

     FineTuneConfiguration fineTuneConf = new FineTuneConfiguration.Builder()
             .seed(seed)
             .iterations(iterations)
             .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
             .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
             .gradientNormalizationThreshold(1.0)
             .updater(new Adam.Builder().learningRate(1e-3).build())
             .l2(0.00001)
             .activation(Activation.IDENTITY)
             .trainingWorkspaceMode(workspaceMode)
             .inferenceWorkspaceMode(workspaceMode)
             .build();

     ComputationGraph model = new TransferLearning.GraphBuilder(graph)
             .fineTuneConfiguration(fineTuneConf)
             .addLayer("outputs",
                     new Yolo2OutputLayer.Builder()
                             .boundingBoxPriors(priors)
                             .build(),
                     "conv2d_9")
             .setOutputs("outputs")
             .build();

     System.out.println(model.summary(InputType.convolutional(416, 416, 3)));

     ModelSerializer.writeModel(model, "tiny-yolo-voc_dl4j_inference.v1.zip", false);

The channels of the 416x416 input images need to be in RGB order (not BGR), with values normalized within [0, 1].