Hello everyone,

I just wanted to share my project and wanted some feedback on it

Goal: Most image models today are bulky and overkill for basic tasks. This project explores how small we can make image classification models while still keeping them functional by stripping them down to the bare minimum.

Current Progress & Results:

Cat vs Dog Classification: First completed task using a 25,000-image dataset with filter bank preprocessing and compact CNNs.

Achieved up to 86.87% test accuracy with models under 12.5k parameters.

Several models under 5k parameters reached over 83% accuracy, showcasing strong efficiency-performance trade-offs.

CIFAR-10 Classification: Second completed task using the CIFAR-10 dataset. This approach just relies on compact CNN architectures without the filter bank preprocessing.

A 22.11k parameter model achieved 87.38% accuracy.

A 31.15k parameter model achieved 88.43% accuracy.

All code and experiments are available in my GitHub repository: https://github.com/SaptakBhoumik/TinyVision

I would love for you to check out the project and let me know your feedback!

Also, do leave a star if you find it interesting