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

Md. Hefzul Hossain Papon, Shadman Rabby
1/6/2026
cs.CVcs.LG

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Abstract

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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Cite this paper

@article{papon2026experimental,
  title  = {Experimental Comparison of Light-Weight and Deep CNN Models Across Diverse Datasets},
  author = {Md. Hefzul Hossain Papon and Shadman Rabby},
  year   = {2026},
  eprint = {2601.03463},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.03463v1}
}

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