Very deep convolutional networks for large-scale image recognition

In 2014, an interesting contribution for image recognition was presented (for more information refer to: Very Deep Convolutional Networks for Large-Scale Image Recognition, by K. Simonyan and A. Zisserman, 2014). The paper shows that, a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. One model in the paper denoted as D or VGG-16 has 16 deep layers. An implementation in Java Caffe (http://caffe.berkeleyvision.org/) has been used for training the model on the ImageNet ILSVRC-2012 (http://image-net.org/challenges/LSVRC/2012/) dataset, which includes images of 1,000 classes and is split into three sets: training (1.3 million images), validation (50,000 images), and testing (100,000 images). Each image is (224 x 224) on three channels. The model achieves 7.5...