Tinyvision:-Building Ultra-Lightweight Models for Image Tasks(Part-1)

How Small Can Image Classifiers Get? My Experiments with Ultra-Lightweight Models The repo is at https://github.com/SaptakBhoumik/TinyVision . If you find it interesting, leave a star, and feel free to reach out on X at https://x.com/saptakbhoumik or via email at saptakbhoumik.acad@gmail.com . I would love to talk about it. Most image classification work today is about pushing accuracy higher and higher, usually by throwing more parameters at the problem. This project goes in the opposite direction: how small can the model get while still being useful? This post covers two tasks from TinyVision (v3): a cat vs dog classifier built around a handcrafted feature pipeline, and a CIFAR-10 classifier that ditches the filter bank entirely and just bets on compact CNN design. I am still writing the full v3 report, so the CIFAR-10 analysis here is partial, but there is enough to share the core ideas and the important numbers. Part 1: Cat vs Dog The Core Idea: Replace Learned Filters with Handcra