The codes contain the pretrained Deeplearning4j convolutional layers of ConvNet D (VGG 16) and ConvNet E (VGG 19) described in
- Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
The pretrained weights were downloaded from the authors' website. The serialized models and configurations in this repository do not contain the fullly connected layers in the original models. The pretrained convolutional networks can be used as feature extractors.
src/Demo.java
for demonstrationINDArray preprocess(INDArray raw)
substracts the means (R: 123.680, G: 116.779, B: 103.939) from each channel of input image.MultiLayerNetwork getBottomLayers(MultiLayerNetwork net, int k)
returns bottom-k layers of a pretrained convolutional VGG network.
src/FeatureVisualizer.java
: Visualize featuressrc/VGGConvNetD.java
andsrc/VGGConvNetE.java
contain the configurations of convolutional layers of VGG models.model/vgg16.dl4jmodel
andmodels/vgg19.dl4jmodel
contain serialized Deeplearning4j VGG models