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I had fixed the eigenvalue 0.000000 thing i had the other day. Or so i thought. It seems to come up randomly when training a model.

I used to think i needed an even amount of pictures, but this doesn't seem to be the case. Sometimes i need to delete, sometimes i need to make it even to get good results. Removal of one picture can influence the outcome of the training. If i don't get it right the value oscillates between male or female genders.

I currently have 600 female pictures, 290 male pictures. Gender classification works very well. But yesterday i had an even better model, but somehow i deleted that (figures).

Anyway i guess what i'm trying to say is, i wish i had a better grasp on what influences this.

PS this is not haartraining, i'm use the facerecognizer model->train.

I had fixed the eigenvalue 0.000000 thing i had the other day. Or so i thought. It seems to come up randomly when training a model. I don't know what is causing that to appear, i wish i did.

I used to think i needed an even amount of pictures, but this doesn't seem to be the case. Sometimes i need to delete, sometimes i need to make it even to get good results. Removal of one picture can influence the outcome of the training. If i don't get it right the value oscillates between male or female genders.

I currently have 600 female pictures, 290 male pictures. Gender classification works very well. But yesterday i had an even better model, but somehow i deleted that (figures).

Anyway i guess what i'm trying to say is, i wish i had a better grasp on what influences this.

PS this is not haartraining, i'm use the facerecognizer model->train.

I had fixed the eigenvalue 0.000000 thing i had the other day. Or so i thought. It seems to come up randomly when training a model. I don't know what is causing that to appear, i wish i did.

I used to think i needed an even amount of pictures, but this doesn't seem to be the case. Sometimes i need to delete, sometimes i need to make it even to get good results. Removal of one picture can influence the outcome of the training. If i don't get it right the value oscillates between male or female genders.

I currently have 600 female pictures, 290 male pictures. pictures, all aligned. Gender classification works very well. But yesterday i had an even better model, but somehow i deleted that (figures).

Anyway i guess what i'm trying to say is, i wish i had a better grasp on what influences this.

PS this is not haartraining, i'm use the facerecognizer model->train.

I had fixed the eigenvalue 0.000000 thing i had the other day. Or so i thought. It seems to come up randomly when training a model. I don't know what what is causing that to appear, i wish i did.did.

I used to think i needed an even amount of pictures, but this doesn't seem to be the case. Sometimes i need to delete, sometimes i need to make it even to get good results. Removal of one picture can influence the outcome of the training. If i don't get it right the value oscillates between male or female genders.

I currently have 600 female pictures, 290 male pictures, all aligned. Gender classification works very well. But yesterday i had an even better model, but somehow i deleted that (figures).

Anyway i guess what i'm trying to say is, i wish i had a better grasp on what influences this. As i'd want to build an even better model, with more pictures. But as long as i don't know what causes the 0.00000 or wild oscillation between labels, there's not much point. It's not the ratio of pictures, it seems, so i guess that leaves the type of pictures.

Is there something messing up my model? There's a thumbnail of someone, which is black when in thumbnail but when i open it , it shows fine. Must be a jpg thing (i save them as png when realigning). Face is also extracted and landmarks applied. So that can't be it.

PS this is not haartraining, i'm use the facerecognizer model->train.