Title: Residual Squeeze VGG16,
Abstract: Deep learning has given way to a new era of machine learning, apart from
computer vision. Convolutional neural networks have been implemented in image
classification, segmentation and object detection. Despite recent advancements,
we are still in the very early stages and have yet to settle on best practices
for network architecture in terms of deep design, small in size and a short
training time. In this work, we propose a very deep neural network comprised of
16 Convolutional layers compressed with the Fire Module adapted from the
SQUEEZENET model. We also call for the addition of residual connections to help
suppress degradation. This model can be implemented on almost every neural
network model with fully incorporated residual learning. This proposed model
Residual-Squeeze-VGG16 (ResSquVGG16) trained on the large-scale MIT
Places365-Standard scene dataset. In our tests, the model performed with
accuracy similar to the pre-trained VGG16 model in Top-1 and Top-5 validation
accuracy while also enjoying a 23.86% reduction in training time and an 88.4%
reduction in size. In our tests, this model was trained from scratch.