Image Classification with Transfer Learning Using a Convolutional Neural Network

Merjem Bajramović, E. Žunić
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Abstract

Paper covers image classification using the Keras API in TensorFlow. The dataset used is a set of labelled images consisting of characters from the Pokémon media franchise. In order to artificially generate additional data, the process of data augmentation has been applied on the initial dataset to reduce overfitting. A comparison between DenseNet-121, DenseNet-169 and DenseNet-201 has been made to observe which of the models scores a greater accuracy. A Graphics Processing Unit (GPU) has been set up to work with TensorFlow in order to efficiently train the model.
基于卷积神经网络迁移学习的图像分类
论文涵盖了使用TensorFlow中的Keras API进行图像分类。使用的数据集是一组标记图像,由来自poksammon媒体特许经营的字符组成。为了人为地产生额外的数据,在初始数据集上应用了数据增强的过程,以减少过拟合。对DenseNet-121、DenseNet-169和DenseNet-201进行了比较,以观察哪一种模型得分更高。为了有效地训练模型,已经建立了一个图形处理单元(GPU)来与TensorFlow一起工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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