How I took my mediocre FashionMNIST model and supercharged it with MobileNetV2 & Transfer Learning — results inside!

Hey folks! 👋

I wanted to share a milestone in my ML learning journey that I think others might find useful (and a bit motivating too).

I first trained a simple fully connected neural net on the classic Fashion MNIST dataset (28×28 grayscale). While the model learned decently, the test accuracy maxed out around 84%. I was stuck with overfitting, no matter how I tweaked layers or regularization.

Then I tried something new: Transfer Learning. I resized the dataset to RGB (96×96), loaded MobileNetV2 with imagenet weights, and added my own classifier layers on top. Guess what?

✅ Test accuracy jumped past 92% ✅ Training time reduced significantly ✅ Model generalized beautifully

This experience taught me that:

You don’t need to train huge models from scratch to get great results.

Pre-trained models act like « knowledge containers » — you’re standing on the shoulders of giants.

FashionMNIST isn’t just a beginner’s dataset — it’s great for testing architecture improvements.

Happy to share the code or walk through the setup if anyone’s curious. Also planning to deploy it on Hugging Face soon!

Would love feedback or similar experiences — what dataset-model combos surprised you the most?

First model :

https://huggingface.co/spaces/lalmasala/apparelclassifier

Second model:

https://huggingface.co/spaces/lalmasala/apparelclassifiernew