Hi, Thank you Jordi, I managed to do what I wanted for the ANN, indeed the algorithm is less obvious to use and less adapted to the classic classifications, but I am not dissatisfied with being any more surrounded in front of a long list of settings !
There is however the GradientBoostTree classifier which remains blocked at 0% writing on Monteverdi 6.0 and 5.10, i don't understand why.. Arthur Le jeudi 15 juin 2017 10:48:57 UTC+2, jordi.inglada a écrit : > > On Wed 14-Jun-2017 at 15:07:46 +0200, "Arthur B." <[email protected] > <javascript:>> wrote: > > I also would like to try the "Artificial Neural Network Classifier" but > i'm a little overwhelmed by the quantity of settings to be adjusted, and > have difficulties in taking out the head of documentation (which is a > little tricky without any artificial neural network knowledge). I would > especially like > > to know quickly if this classification can suit our objectives, and > default settings aren't enough to run correctly the processing. Can you > help me with the esential elements to be parametrized to have basic but > correct results (depending on the image, dataset or expected results i > > guess..) ? > > > > Hi, > > For most parameters, the default values are OK. Since OTB uses OpenCV > for the ANN implementation, you can get more information here: > > http://docs.opencv.org/2.4/modules/ml/doc/neural_networks.html > > The only parameter you need to really play with at the beginning is the > network architechture (how many neurons per hidden layer). This is done > using: > > -classifier.ann.sizes <string list> Number of neurons in each > intermediate layer (mandatory) > > The input layer will have a number of neurons equal to the number of > input bands. You don't need to give this value on the sizes list. The > ouput layer has always as many neurons as the number of classes of your > problem. This is automatically found by the application. So for a > network working on 4 band images and 6 classes, if you choose 2 hidden > layers with 5 neurons per layer, the network architecture is represented > by: > > 4 5 5 6 > > but you only have to give the hidden layers to the application: > > -classifier.ann.sizes 5 5 > > In the GUI application or in Monteverdi, you click twice on "+" and set > 5 and 5. > > I would suggest starting with one single hidden layer and as many > neurons as classes and proceed from there by adding neurons or layers. > > However, bear in mind that for typical image classification problems, > this kind of ANN (multi-layer perceptrons) will give you worse results > than Random Forest or SVM. > > Best wishes, > > Jordi > > > > > Thanks a lot ! > > > > Arthur > > > > -- > -- -- Check the OTB FAQ at http://www.orfeo-toolbox.org/FAQ.html You received this message because you are subscribed to the Google Groups "otb-users" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [email protected] For more options, visit this group at http://groups.google.com/group/otb-users?hl=en --- You received this message because you are subscribed to the Google Groups "otb-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
