Hey everyone!

A little update on VisionQC!

If you've come across my previous post, you'll know that I started this project with a simple idea: build an AI-based system that can detect defective bottles using computer vision.

At the time, I was experimenting with convolutional autoencoders, training them on normal bottle images and using reconstruction errors to identify defects.

Well, things have moved forward a bit since then!

So, what changed?

After spending more time researching anomaly detection approaches and experimenting with the initial model, I decided to give PatchCore a shot.

I'm using the MVTec AD Bottle dataset, and PatchCore seemed like a promising approach for this particular problem.

Without getting too deep into the technical details, PatchCore works by extracting features from image patches and building a memory bank of normal patterns.…