Experimental Comparison of Light-Weight and Deep CNN Models Across Diverse Datasets
By: Md. Hefzul Hossain Papon, Shadman Rabby
Potential Business Impact:
Makes smart cameras work well everywhere, even with less power.
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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