Section: User Commands (1)Updated: May 2008Local indexUp
NAME
opencv-haartraining - train classifier
SYNOPSIS
opencv-haartraining [options]
DESCRIPTION
opencv-haartraining
is training the classifier. While it is running, you can already get an
impression, whether the classifier will be suitable or if you need to improve
the training set and/or parameters.
In the output:
'POS:'
shows the hitrate in the set of training samples (should be equal or near to
1.0
as in stage 0)
'NEG:'
indicates the false alarm rate (should reach at least
5*10-6
to be a usable classifier for real world applications)
If one of the above values gets
0 (zero)
there is an overflow. In this case the false alarm rate is so low, that
further training doesn't make sense anymore, so it can be stopped.
OPTIONS
opencv-haartraining
supports the following options:
-data dir_name
The directory in which the trained classifier is stored.
-vec vec_file_name
The file name of the positive samples file (e.g. created by the
opencv-createsamples(1)
utility).
-bg background_file_name
The background description file (the negative sample set). It contains a list
of images into which randomly distorted versions of the object are pasted for
positive sample generation.
-npos number_of_positive_samples
The number of positive samples used in training of each classifier stage.
The default is
2000.
-nneg number_of_negative_samples
The number of negative samples used in training of each classifier stage.
The default is
2000.
Reasonable values are
-npos 7000 -nneg 3000.
-nstages number_of_stage
The number of stages to be trained. The default is
14.
-nsplits number_of_splits
Determine the weak classifier used in stage classifiers. If the value is
1,
then a simple stump classifier is used
>=2,
then CART classifier with
number_of_splits
internal (split) nodes is used
The default is
1.
-mem memory_in_MB
Available memory in
MB
for precalculation. The more memory you have the faster the training process is.
The default is
200.
-sym, -nonsym
Specify whether the object class under training has vertical symmetry or not.
Vertical symmetry speeds up training process and reduces memory usage. For
instance, frontal faces show off vertical symmetry. The default is
-sym.
-minhitrate min_hit_rate
The minimal desired hit rate for each stage classifier. Overall hit rate may
be estimated as
min_hit_rate^number_of_stages.
The default is
0.950000.
-maxfalsealarm max_false_alarm_rate
The maximal desired false alarm rate for each stage classifier. Overall false
alarm rate may be estimated as
max_false_alarm_rate^number_of_stages.
The default is
0.500000.
-weighttrimming weight_trimming
Specifies whether and how much weight trimming should be used. The default is
0.950000.
A decent choice is
0.900000.
-eqw
Specify if initial weights of all samples will be equal.
-mode {BASIC|CORE|ALL}
Select the type of haar features set used in training.
BASIC
uses only upright features, while
CORE
uses the full upright feature set and
ALL
uses the full set of upright and 45 degree rotated feature set.
The default is
BASIC.
The type of the applied boosting algorithm. You can choose between Discrete
AdaBoost (DAB), Real AdaBoost (RAB), LogitBoost (LB) and
Gentle AdaBoost (GAB). The default is
GAB.
-err {misclass|gini|entropy}
The type of used error if Discrete AdaBoost (-bt DAB) algorithm is
applied. The default is
misclass.
