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I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascade using the output of opencv_transcascade as it is being generated. The reason being a) I'm curious, b) I'd like to be able to stop it from continuing if something looks 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.
• 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|
+----+---------+---------+
END

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

I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascade using the output of opencv_transcascade as it is being generated. The reason being a) I'm curious, b) I'd like to be able to stop it from continuing if something looks 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
• 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|
+----+---------+---------+
END

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

I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascade using the output of opencv_transcascade as it is being generated. The reason being a) I'm curious, b) I'd like to be able to stop it from continuing if something looks 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
• 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|
+----+---------+---------+
END

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

I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascade using the output of opencv_transcascade as it is being generated. The reason being a) I'm curious, b) I'd like to be able to stop it from continuing if something looks 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
• resulting file size of cascade.xml
• 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|
+----+---------+---------+
END

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

I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascade using the output of opencv_transcascade as it is being generated. The reason being a) I'm curious, b) I'd like to be able to stop it from continuing if something looks 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
• resulting small file size of cascade.xmlfinal 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|
+----+---------+---------+
END

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

I am wondering if someone could help me draw some conclusions about the quality of my HAAR cascade cascades using the output of opencv_transcascade as it is they are being generated. The reason being a) I'm curious, b) I'd like to be able to stop it from continuing if something looks funky.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|
+----+---------+---------+
END

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