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[P] image classification for product images (alien.top)

submitted 10 months ago by EyeTechnical7643@alien.top to c/machinelearning@academy.garden

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Say you are a potato chips company. The goal is to have consumers upload images of the product they are having issues with and be able to identify the product by brand/variant using machine learning. Consumers can upload real product photos that they have taken, or upload bogus images from the internet, or even upload completely irrelevant/inappropriate photos (like that of a dog or cat).

In this example, for the legitimate image, the goal is to classify it as "Lays Classic". There might be products that are not in bag form, such as those in tubes. Furthermore, the images taken can be in different lighting conditions/orientations. Some images might have other products as well.

I have been out of the ML field for the past 4 years so I'm not up to date on the most state of the art methods for this problem. I have studied CNNs 4 years ago, but there has been advances like transformer based methods. Someone has tried ResNet-50 and YOLOv5, and I'm thinking about using a pretrained model like CLIP and just train the final classification layer.

But I would appreciate to hear from someone more well versed what recommended approach to take as far as model/labeling/number of images needed per class, etc. It might be that I would need multiple models, such as one to identify the legitimate images from the rest, and then another one to identify the product/variants.

Any advice would be welcome. Thanks

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[–] londons_explorer@alien.top 1 points 10 months ago

For detecting "bogus image from the internet", just search your own database of submitted images. If you see the same image (either by md5 or by very similar embedding vector), then it's probably one from the internet. Before long, you'll have collected all the ones users can easily find from Google Images etc. (I assume this is to prevent fraud where a user says "look, my packet was damaged", when in fact they just searched for a picture of a damaged pack online)