Blog

Learn About Our Meetup

4500+ Members

[D] What are good heuristics when choosing classes for image classification?

For example let’s say I want to classify eggs. And eggs in images can often be seen as just an egg, eggs in an egg cartoon and the carton can be closed or opened.

A naive approach would be to put all these image under the class eggs. But it might work better if there are 2 classes one for eggs and one for eggs in carton so training should be easier since these can look quite different since a group of eggs looks much different from a closed carton of eggs. I also feel like separating the classes can have unwanted outcomes like separating contexts. For example eggs withing cartons could rely on context of being in a kitchen and grocery store so it may less accurately predict an image has eggs if it is a carton of eggs in a farm.

Is my thinking correct on this?

What has been your experience with similar situations?

This question specifically focuses on image classification using neural nets.

submitted by /u/Kerlin_Michel
[link] [comments]

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.