标题： 轻量级和深度CNN模型在多样化数据集上的实验比较 显示英文标题

标题： Experimental Comparison of Light-Weight and Deep CNN Models Across Diverse Datasets

摘要： 我们的结果表明，一个经过良好正则化的浅层架构可以在不同领域中作为高度有竞争力的基线，从智慧城市监控到农业品种分类，而无需使用大型GPU或专用预训练模型。这项工作为多个孟加拉国视觉数据集建立了统一、可重复的基准，并突显了轻量级CNN在低资源环境中实际部署的价值。 显示英文摘要

摘要： Our results reveal that a well-regularized shallow architecture can serve as a highly competitive baseline across heterogeneous domains - from smart-city surveillance to agricultural variety classification - without requiring large GPUs or specialized pre-trained models. This work establishes a unified, reproducible benchmark for multiple Bangladeshi vision datasets and highlights the practical value of lightweight CNNs for real-world deployment in low-resource settings.