Effectiveness of Transfer Learning and Fine Tuning in Automated Fruit Image Classification

Abstract

Automated fruit image classification is a challenging problem. The study presented (in this paper) analyzes the effectiveness of transfer learning and fine tuning in improving classification accuracy for this problem. For this purpose, Inception v3 and VGG16 models are exploited. The dataset used in this study is the Fruits 360 dataset containing 72 classes and 48,249 images. The paper presents experiments that prove that transfer learning and fine tuning can significantly improve fruit image classification accuracy. Transfer learning using VGG16 model has been demonstrated to give the best classification accuracy of 99.27%. Experiments have also shown that fine tuning using VGG16 and transfer learning using Inception v3 also produce quite impressive fruit image classification accuracies. Not only is the effectiveness of transfer learning and fine tuning demonstrated through experiments, but a self-designed 14-layer convolutional neural net has also proven to be exceptionally good at the task with classification accuracy of 96.79%.