Purpose

Because of the fast-growing digital image collections on online platforms and the transfer learning ability of deep learning technology, image classification could be improved and implemented for the hostel domain, which has complex clusters of image contents. This paper aims to test the potential of 11 pretrained convolutional neural network (CNN) with transfer learning for hostel image classification on the first hostel image database to advance the knowledge and fill the gap academically, as well as to suggest an alternative solution in optimal image classification with less labour cost and human errors to those who manage hostel image collections.

Design/methodology/approach

The hostel image database is first created with data pre-processing steps, data selection and data augmentation. Then, the systematic and comprehensive investigation is divided into seven experiments to test 11 pretrained CNNs which transfer learning was applied and parameters were fine-tuned to match this newly created hostel image dataset. All experiments were conducted in Google Colaboratory environment using PyTorch.

Findings

The 7,350 hostel image database is created and labelled into seven classes. Furthermore, its experiment results highlight that DenseNet 121 and DenseNet 201 have the greatest potential for hostel image classification as they outperform other CNNs in terms of accuracy and training time.

Originality/value

The fact that there is no existing academic work dedicating to test pretrained CNNs with transfer learning for hostel image classification and no existing hostel image-only database have made this paper a novel contribution.

中文翻译：

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用于宿舍图像分类的迁移学习

目的

由于在线平台上快速增长的数字图像集合和深度学习技术的迁移学习能力，可以改进和实施具有复杂图像内容集群的宿舍域的图像分类。本文旨在测试 11 个预训练卷积神经网络 (CNN) 与迁移学习在第一个宿舍图像数据库上进行宿舍图像分类的潜力，以推进知识并填补学术空白，并提出最佳图像的替代解决方案对管理旅馆图像集的人员而言，可以减少人工成本和人为错误的分类。

设计/方法/方法

宿舍图像数据库首先通过数据预处理步骤、数据选择和数据增强来创建。然后，系统和全面的调查分为七个实验，以测试 11 个预训练的 CNN，这些 CNN 应用了迁移学习，并微调了参数以匹配这个新创建的宿舍图像数据集。所有实验均使用 PyTorch 在 Google Colaboratory 环境中进行。

发现

创建了 7,350 个旅馆图像数据库并将其标记为七个类别。此外，其实验结果强调 DenseNet 121 和 DenseNet 201 在准确度和训练时间方面优于其他 CNN，因此在宿舍图像分类方面具有最大潜力。

原创性/价值

没有现有的学术工作专门用于测试预训练的 CNN 并使用迁移学习进行宿舍图像分类，也没有现有的仅宿舍图像数据库，这一事实使本文做出了新的贡献。