轻量CNN在多种数据集上表现媲美深度模型，适合低资源场景。
Experimental Comparison of Light-Weight and Deep CNN Models Across Diverse Datasets
- 用正则化浅层CNN构建统一基准，无需大算力或预训练模型。
- 在城市监控与农业分类等多领域表现优异，跨域适应性强。
- 特别适合算力有限的现实部署，如发展中国家的视觉应用。
实验表明，经过良好正则化的浅层网络架构可在异构领域（从智慧城市监控到农作物品种分类）中作为极具竞争力的基线，无需大型GPU或专用预训练模型。本工作为多个孟加拉国视觉数据集建立了统一、可复现的基准，并突显了轻量级CNN在低资源环境下的实际应用价值。
原文摘要 · Abstract (English)
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.
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