CODEWITHSUNDEEP
machine-learningneural-networkcomputer-visiondeep-learningcaffe
alfa_80
alfa_80

Reputation: 427

Caffe's way of representing negative examples on benchmark dataset for binary classification

I would like to know how to define or represent a negative training set if I would want to train a binary classifier from a pre-trained model say, AlexNet on ILSVRC12 (or ImageNet) dataset. What I am currently thinking of is to take one the classes which is not related as the negative training set while the one which is related as positive one. Is there any better way which is more elegant?

Upvotes: 0

Views: 100

Answers (1)

Prune
Prune

Reputation: 77860

The CNNs trained on the ILSVRC data set are already discriminating among 1000 classes of images. Yes, you can use one of those topologies to train a binary classifier, but I suggest that you start with an untrained model and run it through your two chosen classes. If you start with a trained model, you have to unlearn a lot, and your result is still trying to discriminate among 1000 classes: that last FC layer is going to give you trouble.

There are ways to work around the 1000-class problem. If your application already overlaps one or more of the trained classes, then simply add a layer that maps those classes to label "1" and all the others to label "0".

If you're insistent on retaining the trained kernels, then try replacing the final FC layer (1000) with a 2-class FC layer. Then choose your two classes (applicable images vs everything else) and run your training.

Upvotes: 1

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