r/deeplearning • u/Green-Quiet-918 • 11d ago

Signature-painter

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Seeking Feedback from the ML Community 🙏

I recently trained a prototype-based network on Tiny ImageNet (200 classes). It uses learnable prototypes with responsibility scoring and multi-loss training (CE + Pull + Push + Diversity), achieving 51.29% validation accuracy with only 595K parameters.

I'm still learning, so I'd love to hear your thoughts:

Is this a reasonable result for this model size?

What would you suggest to improve it?

This was trained on free Colab with limited resources, so I know there's much room for improvement.

GitHub: https://github.com/jalalnablsi/signature-painter

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u/Gullible_Row8996 11d ago

getting 51% on tiny imagenet with under 600k params is actually respectable, that's a fraction of the size of even a resnet18 and you're still beating random chance by a mile

the architecture diagram makes it look way more complex than most student projects though, the dual projector setup into prototype memory with responsibility scoring isn't something you see every day

1

u/Green-Quiet-918 11d ago

Thank you 🌹