Training Independent Object Detection Models

Question

I'm working on a research project for detecting and segmenting two different defects in a material given an input image of such material.

I started by focusing on one defect since it was predominant in the training set. I implemented the MaskRCNN (Matterport) model and adapted for PNG annotation masks. It works really well after spending some time fine tuning it.

It might be naive/easy for most of you but my question is:

• Is it preferable/advantageous to train independent models for each class of objects (two models, each for each defect) ifvcomputational time/power is not a limitation? I would feed an image to both models in parallel and one would return the instances of defect 1 and the other the instances of defect 2.

The reason for this question is that I have the feeling that if you train a single model for multi-class detection it can happen that when trying to minimize losses, since you are optimizing the overall loss, you are optimizing weights for working fine for both classes but you are not optimizing the weights and losses separately for each class and you might loss some detection/segmentation accuracy.

A common approach would be to try both alternatives: 1. single model for both classes and 2. two independent models for two classes.

I will eventually implement both alternatives and compare them. However, I want to know if the second alternative has already been tested and what has been the experience in order to properly justify this alternative if a paper comes out of this research.

Answer 1

In most of the cases if you train a separate model for each of the classes it would improve the performance when you have many classes and computation resources is not an issue. But as I guess, you have only two classes, so by training two different models you would not see much improvements in the accuracy. You can try both approaches but you will be beneficial when have many classes to detect.