Abstract
This study presents a comprehensive comparative analysis of custom-built Convolutional Neural Networks (CNNs) against popular pre-trained architectures (ResNet-18 and VGG-16) using both feature extraction and transfer learning approaches. We evaluated these models across five diverse image classification datasets from Bangladesh: Footpath Vision, Auto Rickshaw Detection, Mango Image Classification, Paddy Variety Recognition, and Road Damage Detection. Our experimental results demonstrate that transfer learning with fine-tuning consistently outperforms both custom CNNs built from scratch and feature extraction methods, achieving accuracy improvements ranging from 3% to 76% across different datasets. Notably, ResNet-18 with fine-tuning achieved perfect 100% accuracy on the Road Damage BD dataset. While custom CNNs offer advantages in model size (3.4M parameters vs. 11-134M for pre-trained models) and training efficiency on simpler tasks, pre-trained models with transfer learning provide superior performance, particularly on complex classification tasks with limited training data. This research provides practical insights for practitioners in selecting appropriate deep learning approaches based on dataset characteristics, computational resources, and performance requirements.
Abstract (translated)
这项研究提供了一个全面的比较分析，对比了自定义构建的卷积神经网络（CNN）与流行的预训练架构（ResNet-18和VGG-16），使用特征提取和迁移学习两种方法。我们对五种来自孟加拉国的不同图像分类数据集进行了这些模型的评估：Footpath Vision、Auto Rickshaw Detection、Mango Image Classification、Paddy Variety Recognition以及Road Damage Detection。 我们的实验结果表明，通过微调进行迁移学习在所有情况下都优于从头开始构建的自定义CNN和特征提取方法，在不同的数据集中准确率提高了3%到76%。值得注意的是，经过微调后的ResNet-18模型在Road Damage BD数据集上实现了完美的100%精度。 尽管从头开始构建的CNN在网络大小（参数量为3.4M）和处理较为简单任务时的训练效率方面具有优势，但预训练模型结合迁移学习方法能够提供更优的表现，尤其是在处理复杂分类任务且训练数据有限的情况下。这项研究为实践者提供了基于数据集特性、计算资源以及性能需求来选择适当的深度学习策略的实际见解。
URL
https://arxiv.org/abs/2601.04352
