Building Ultra-Lightweight Image Classifiers with TinyVision (Part 1)

This article explores how small image classification models can get while remaining effective. Using handcrafted feature pipelines and compact CNN architectures, the experiments show that models with just a few thousand parameters can achieve competitive accuracy. It also highlights the role of pooling strategies and architectural design in improving efficiency without scaling model size.