Zoo Models
Available models
AlexNet
AlexNet
Dl4j’s AlexNet model interpretation based on the original paper ImageNet Classification with Deep Convolutional Neural Networks and the imagenetExample code referenced. References: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/train_val.prototxt
Model is built in dl4j based on available functionality and notes indicate where there are gaps waiting for enhancements.
Bias initialization in the paper is 1 in certain layers but 0.1 in the imagenetExample code Weight distribution uses 0.1 std for all layers in the paper but 0.005 in the dense layers in the imagenetExample code
Darknet19
Darknet19 Reference: https://arxiv.org/pdf/1612.08242.pdf ImageNet weights for this model are available and have been converted from https://pjreddie.com/darknet/imagenet/ using https://github.com/allanzelener/YAD2K .
There are 2 pretrained models, one for 224x224 images and one fine-tuned for 448x448 images. Call setInputShape() with either {3, 224, 224} or {3, 448, 448} before initialization. The channels of the input images need to be in RGB order (not BGR), with values normalized within [0, 1]. The output labels are as per https://github.com/pjreddie/darknet/blob/master/data/imagenet.shortnames.list .
FaceNetNN4Small2
A variant of the original FaceNet model that relies on embeddings and triplet loss. Reference: https://arxiv.org/abs/1503.03832 Also based on the OpenFace implementation: http://reports-archive.adm.cs.cmu.edu/anon/2016/CMU-CS-16-118.pdf
InceptionResNetV1
A variant of the original FaceNet model that relies on embeddings and triplet loss. Reference: https://arxiv.org/abs/1503.03832 Also based on the OpenFace implementation: http://reports-archive.adm.cs.cmu.edu/anon/2016/CMU-CS-16-118.pdf
LeNet
LeNet was an early promising achiever on the ImageNet dataset. References:
MNIST weights for this model are available and have been converted from https://github.com/f00-/mnist-lenet-keras.
NASNet
Implementation of NASNet-A in Deeplearning4j. NASNet refers to Neural Architecture Search Network, a family of models that were designed automatically by learning the model architectures directly on the dataset of interest.
This implementation uses 1056 penultimate filters and an input shape of (3, 224, 224). You can change this.
Paper: https://arxiv.org/abs/1707.07012 ImageNet weights for this model are available and have been converted from https://keras.io/applications/.
ResNet50
Residual networks for deep learning.
Paper: https://arxiv.org/abs/1512.03385 ImageNet weights for this model are available and have been converted from https://keras.io/applications/</a>.
SimpleCNN
A simple convolutional network for generic image classification. Reference: https://github.com/oarriaga/face_classification/
SqueezeNet
U-Net
An implementation of SqueezeNet. Touts similar accuracy to AlexNet with a fraction of the parameters.
Paper: https://arxiv.org/abs/1602.07360 ImageNet weights for this model are available and have been converted from https://github.com/rcmalli/keras-squeezenet/.
TextGenerationLSTM
LSTM designed for text generation. Can be trained on a corpus of text. For this model, numClasses is
Architecture follows this implementation: https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py
Walt Whitman weights are available for generating text from his works, adapted from https://github.com/craigomac/InfiniteMonkeys.
TinyYOLO
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); }</pre>
The channels of the 416x416 input images need to be in RGB order (not BGR), with values normalized within [0, 1].
UNet
U-Net
An implementation of U-Net, a deep learning network for image segmentation in Deeplearning4j. The u-net is convolutional network architecture for fast and precise segmentation of images. Up to now it has outperformed the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Paper: https://arxiv.org/abs/1505.04597 Weights are available for image segmentation trained on a synthetic dataset
VGG16
VGG-16, from Very Deep Convolutional Networks for Large-Scale Image Recognition https://arxiv.org/abs/1409.1556 Deep Face Recognition http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf
ImageNet weights for this model are available and have been converted from https://github.com/fchollet/keras/tree/1.1.2/keras/applications. CIFAR-10 weights for this model are available and have been converted using “approach 2” from https://github.com/rajatvikramsingh/cifar10-vgg16. VGGFace weights for this model are available and have been converted from https://github.com/rcmalli/keras-vggface.
VGG19
VGG-19, from Very Deep Convolutional Networks for Large-Scale Image Recognition https://arxiv.org/abs/1409.1556 ImageNet weights for this model are available and have been converted from https://github.com/fchollet/keras/tree/1.1.2/keras/applications.
Xception
U-Net
An implementation of Xception in Deeplearning4j. A novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions.
Paper: https://arxiv.org/abs/1610.02357 ImageNet weights for this model are available and have been converted from https://keras.io/applications/.
YOLO2
YOLOv2 Reference: https://arxiv.org/pdf/1612.08242.pdf
ImageNet+COCO 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 = “yolo.h5”;
KerasLayer.registerCustomLayer(“Lambda”, KerasSpaceToDepth.class);
ComputationGraph graph = KerasModelImport.importKerasModelAndWeights(filename, false);
INDArray priors = Nd4j.create(priorBoxes);
FineTuneConfiguration fineTuneConf = new FineTuneConfiguration.Builder()
.seed(seed)
.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_23”)
.setOutputs(“outputs”)
.build();
System.out.println(model.summary(InputType.convolutional(608, 608, 3)));
ModelSerializer.writeModel(model, “yolo2_dl4j_inference.v1.zip”, false); }The channels of the 608x608 input images need to be in RGB order (not BGR), with values normalized within [0, 1].
pretrainedUrl
public String pretrainedUrl(PretrainedType pretrainedType) Default prior boxes for the model
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