Deep Learning for Nanoscience Scanning Electron Microscope Image Recognition

In this thesis, part of the NFFA-Europe project, different deep learning techniques are used in order to train several neural networks on high performance computing facilities with the goal of classifying images of nanoscience structures captured by SEM (scanning electron microscope). Using TensorFlow and TF-Slim as deep learning frameworks, we train on multiple and different GPU cards several state-of-the-art convolutional neural network (CNN) architectures (i.e. AlexNet, Inception, ResNet, DenseNet) and test their performances in terms of training time and accuracy on our SEM dataset. Furthermore, we coded a DenseNet implementation in TF-Slim. Moreover, we apply and benchmark transfer learning, which consists of retraining some pre-trained models. We then present some preliminary results, obtained in collaboration with Intel and CINECA, about several tests on Neon, the deep learning framework by Intel and Nervana-Systems optimized on Intel CPUs. Lastly, Inceptionv3 was ported from TF-Slim to Neon for future investigations.