I have an idea but I am not certain that it can be modeled in a DL architecture.

Let's say we have images of different qualities based on color patterns and their assessment as labels in a range from 0-1. E.g. Image 1 has 0.25 quality, Image 2 has 0.5 quality and so on.

Could this be implemented in a standard Resnet50 architecture with 1 output and sigmoid? Is there anything in literature that you could point me up to? I wasn't able to find anything, maybe I am searching it wrong.

EDIT: I have found this https://github.com/idealo/image-quality-assessment, but the implementation is different that I suggest. I know that this can work if I add five different quality classes.

I want to know if I can train this with 1 class output with a rank, in order to let the model understand how those qualities are linked. For instance quality 2 is the next best quality than quality 1.

  • $\begingroup$ Could you maybe elaborate what quality is in this case? Are you fi interested in an assessment on a technical level (the amount of noise for instance), functional (can I distinguish what it is) or artistic (Is it interesting)? $\endgroup$ Mar 5 '20 at 11:38
  • $\begingroup$ Ok I see you aded a little more context. How will you obtain these assessments? $\endgroup$ Mar 5 '20 at 11:44
  • 1
    $\begingroup$ Manual annotation on the images. What the user assess as Quality 1, 2 etc. $\endgroup$ Mar 5 '20 at 11:51
  • $\begingroup$ @SvanBalen Manual annotation. Please check also the second edit. $\endgroup$ Mar 5 '20 at 11:56
  • $\begingroup$ Is that assessment in anyway guided (with a briefing for instance) or is it more like broad stroked "I like this image"? $\endgroup$ Mar 5 '20 at 13:13

I found the answer I was looking for in this Google paper:

However, in the case of ordered-classes (e.g. aesthetic and quality estimation), cross-entropy loss lacks the inter-class relationships between score buckets. One might argue that ordered-classes can be represented by a real number, and consequently, can be learned through a regression framework. Yet, it has been shown that for ordered classes, the classification frameworks can outperform regression models [21], [31]. Hou et al. [21] show that training on datasets with intrinsic ordering between classes can benefit from EMDbased losses. These loss functions penalize mis-classifications according to class distances


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