Hello all,
I'm trying to make kind of quick & dirty test of LBP for purpose of my work. I'm feeding trainer with synthetic dataset, for now I tried to do this two times. First try ended with "out of memory" situation for my system (macbook pro, mem = 8gb). That was 18 training samples (18pos/18neg) with 300x300px resolution. I generated new set with 90x90px samples.
opencv_traincascade -data data -vec object.vec -bg neg/background.txt -numPos 18 -numNeg 18 -numStages 2 -w 90 -h 90 -featureType LBP
PARAMETERS:
cascadeDirName: data
vecFileName: ardrone2b.vec
bgFileName: asus_camera_simple/background.txt
numPos: 18
numNeg: 18
numStages: 2
precalcValBufSize[Mb] : 256
precalcIdxBufSize[Mb] : 256
stageType: BOOST
featureType: LBP
sampleWidth: 90
sampleHeight: 90
boostType: GAB
minHitRate: 0.995
maxFalseAlarmRate: 0.5
weightTrimRate: 0.95
maxDepth: 1
maxWeakCount: 100
===== TRAINING 0-stage =====
<BEGIN
POS count : consumed 18 : 18
NEG count : acceptanceRatio 18 : 1
Precalculation time: 1
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 0|
+----+---------+---------+
END>
Training until now has taken 0 days 0 hours 0 minutes 11 seconds.
===== TRAINING 1-stage =====
<BEGIN
POS count : consumed 18 : 18
This training continues already for ~24h. In fact, I would like to know if there any kind of "rule of thumb" hints about choosing dataset parameters vs time for training. Right now I want to achieve fast training (max up to ~1-2h), quality of resulting detector is not important for now.
Thank you in advance.
P.S. Actually any hints on training process would be appreciated, links, etc.