EBLearn is easy to setup and use. To get you started, let us
- Head over to our Google Groups page, where we can easily answer your questions
This makes you familiar with the training/testing pipeline.
You can also check these demos while working with tutorials:
(No C++ programming needed)
By the end of these series of tutorials, you will learn how to build a classifier in EBLearn. That means that you can build face detectors, handwriting recognition systems, etc. The possibilities are unlimited . I recommend that you go through these tutorials in order, so that you do not need to come back a tutorial if you dont understand something.
Note: A general advice when things don't go as expected is to use the “_debug” version of the corresponding tool. For example, detect_debug rather than detect will give a good feedback of what is happening.
The following steps do not involve any programming but rather mere modification of existing scripts and configuration files. All necessary tools are already coded, from dataset compiling to training and classification, detection or regression.
To use your trained machine as a detector, use the detect tool by calling: 'detect face.conf' and make sure the trained network file is specified in the configuration along with an input source.