machine learning - Convolutional Neural Networks with Caffe and NEGATIVE IMAGES -


when training set of classes (let's #clases (number of classes) = n) on caffe deep learning (or cnn framework) , make query caffemodel, % of probability of image ok.

so, let's take picture of similar class 1, , result:

1.- 90%

2.- 10%

rest... 0%

the problem is: when take random picture (for example of environment), i keep getting same result, 1 of class predominant (>90% probability) doesn't belong class.

so i'd hear opinions/answers people has experienced , have solved how deal no-sense inputs neural network.

my purposes are:

  1. train 1 more class negative images (like train_cascade).
  2. train 1 more class positive images in train set, , negative on val set.

but purposes don't have scientific base execute them, that's why ask question.

what do?

thank in advance.

rafael.


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