A Mobile Application for Dog Breed Detection and Recognition Based on Deep Learning
Richard Sinnott, Fang Wu, Wenbin Chen · 2018
Deep learning provides the ability to train algorithms (models) that can tackle the problems of data classification and prediction based on deriving (learning) knowledge from raw data. Convolutional Neural Networks (CNNs) provides one commonly used approach for image classification and detection. In this work we describe a CNN-based method for detecting dogs in potentially complex images and subsequently consider the identification of the type/breed of dogs. The results achieve nearly 85% accuracy for breed classification for a set of 50 classes of dogs and 64% accuracy for 120 other less common dog types. An iOS application and associated big data processing infrastructure utilizing a variety of GPUs was used to support the image classification algorithms.