Drawing conclusions about cascade quality during training

asked 2016-09-30 03:48:40 -0500

roboteyes gravatar image

updated 2016-09-30 04:05:42 -0500

I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascades using the output of opencv_transcascade as they are being generated. The reason being a) I'm curious, b) I'd like to be able to stop wasting resources training on a cascade that is going to end up being 'funky'.

Given a minhitrate of .99 and a false alarm rate of 0.5, properly curated positive and background samples with a 3:1 neg:pos ratio, I have seen:

  • cascades that complete at much earlier stage than expected.
  • stages where I don't see FA fall below 1 until N > 5
  • small file size of final cascade.xml ( < 30kB)
  • cascades where the N (what is this signify?) column exceeds 40, yet others where it rarely exceeds 10
  • things like this (single entry with a zero false alarm rate):
===== TRAINING 9-stage =====
POS count : consumed   2723 : 2817
NEG count : acceptanceRatio    6960 : 0.000287357
Precalculation time: 6
|  N |    HR   |    FA   |
|   1| 0.999265|        0|

So I'm asking, what do some of you pros look for in the training output that help you predict the general quality of

  • input settings
  • positive sample quality
  • background sample quality
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