Abstract
Convolutional Neural Networks (CNNs) have demonstrated remarkable success in image classification tasks; however, the choice between designing a custom CNN from scratch and employing established pre-trained architectures remains an important practical consideration. In this work, we present a comparative analysis of a custom-designed CNN and several widely used deep learning architectures, including VGG-16, ResNet-50, and MobileNet, for an image classification task. The custom CNN is developed and trained from scratch, while the popular architectures are employed using transfer learning under identical experimental settings. All models are evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results show that pre-trained CNN architectures consistently outperform the custom CNN in terms of classification accuracy and convergence speed, particularly when training data is limited. However, the custom CNN demonstrates competitive performance with significantly fewer parameters and reduced computational complexity. This study highlights the trade-offs between model complexity, performance, and computational efficiency, and provides practical insights into selecting appropriate CNN architectures for image classification problems.
Abstract (translated)
卷积神经网络（CNN）在图像分类任务中表现出卓越的成功；然而，是设计自定义的CNN还是使用已有的预训练架构仍然是一个重要的实际考虑因素。在这项工作中，我们对一个定制设计的CNN和几个广泛使用的深度学习架构（包括VGG-16、ResNet-50和MobileNet）进行了比较分析，以进行图像分类任务。该定制的CNN从头开始开发并训练，而流行的架构则在相同的实验设置下通过迁移学习使用。所有模型均根据标准性能指标（如准确率、精确率、召回率和F1分数）进行评估。实验结果表明，在数据集有限的情况下，预训练的CNN架构在分类精度和收敛速度方面始终优于自定义的CNN。然而，定制的CNN在参数数量较少且计算复杂度较低时表现出相当的竞争性能。这项研究强调了模型复杂性、性能和计算效率之间的权衡，并为选择适合图像分类问题的适当CNN架构提供了实用见解。
URL
https://arxiv.org/abs/2601.04397
