Recycling of generic ImageNet-trained models for smart-city applications

Katarzyna Filus, Joanna Domanska

Abstract: Convolutional Neural Networks (CNNs) enabled breakthroughs in computer vision. They are also used in different domains of smart cities to process and analyze large amounts of image data, which is crucial for intelligent decision-making. CNNs trained on large-scale datasets such as ImageNet, possess remarkable image classification abilities. As it takes a lot of time and computational resources to train models on such datasets, we propose a solution to reuse these models in the area of smart-cities. We call our approach the model recycling, as it uses the existing versatile knowledge of ImageNet-trained networks for resource conservation and faster deployment of applications in the smart-city domains. For that purpose, we utilize the semantic connections between ImageNet categories to determine the sets of classes that can be used to create specialized classification models for smart transportation, shopping and education with no additional training. We present a methodology to extract such specialized models from generic CNNs trained on ImageNet. Such models can be used at low budget to create solutions for automatic data annotation and in interactive applications. They can also be used as a starting point for fine-tuning. We also present 2 strategies of creating ensembles with these specialized models for better overall and per-class accuracy. Our general methodology can be also used in other domains outside the smart cities.