Abstract
This paper presents a comparative study of a custom convolutional neural network (CNN) architecture against widely used pretrained and transfer learning CNN models across five real-world image datasets. The datasets span binary classification, fine-grained multiclass recognition, and object detection scenarios. We analyze how architectural factors, such as network depth, residual connections, and feature extraction strategies, influence classification and localization performance. The results show that deeper CNN architectures provide substantial performance gains on fine-grained multiclass datasets, while lightweight pretrained and transfer learning models remain highly effective for simpler binary classification tasks. Additionally, we extend the proposed architecture to an object detection setting, demonstrating its adaptability in identifying unauthorized auto-rickshaws in real-world traffic scenes. Building upon a systematic analysis of custom CNN architectures alongside pretrained and transfer learning models, this study provides practical guidance for selecting suitable network designs based on task complexity and resource constraints.
Abstract (translated)
本文介绍了一种定制卷积神经网络（CNN）架构与五个真实世界图像数据集上广泛使用的预训练和迁移学习CNN模型之间的比较研究。这些数据集涵盖了二元分类、细粒度多类识别以及物体检测等场景。我们分析了诸如网络深度、残差连接和特征提取策略等结构因素如何影响分类及定位性能。结果显示，更深的CNN架构在细粒度多类数据集中提供了显著的性能提升，而轻量级的预训练和迁移学习模型对于更简单的二元分类任务仍然非常有效。此外，我们将提出的架构扩展到物体检测场景中，展示了其能够适应识别真实交通场景中的未经授权使用的小型三轮摩托车（auto-rickshaws）。通过对定制CNN架构与预训练及迁移学习模型进行系统的分析，本研究为基于任务复杂度和资源限制选择合适的网络设计提供了实用指导。
URL
https://arxiv.org/abs/2601.01099
