CODEWITHSUNDEEP
tensorflowdeep-learningclassificationobject-detection
Eric Tondelli
Eric Tondelli

Reputation: 113

How to create a class for non classified object in tensorflow?

Hi i have build my CNN with two classes dogs and cats, i have trained this and now i am able to classify dog and cat image. But what about if i want to introduce a class for new unclassified object? For example if i feed my network with a flower image's the network give me a wrong classification. I want to build my network with a third class for new unclassified object. But how can i build this third class. Which images i have to use to get class for new object that are different from dogs or cats? Actually at the end of my network i use Softmax and my code is developed by using tensorflow. Could someone provide me some suggestion? Thanks

Upvotes: 5

Views: 4216

Answers (2)

javidcf
javidcf

Reputation: 59691

You need to add a third "something else" class to your network. There are several ways you can go about it. In general, if you have a class that you want to detect you should have examples for that class, so you could add images without cats or dogs to your training data labelled with the new class. However, this is a bit tricky, because the new class is, by definition, everything in the universe but dogs and cats, so you cannot possibly expect to have enough data to train for it. In practice, though, if you have enough examples the network will probably learn that the third class is triggered whenever the first two are not.

Another option that I have used in the past is to model the "default" class slightly different from the regular ones. So, instead of trying to actually learn what is a "not cat or dog" image, you can just explicitly say that it is just whatever does not activates the cat or dog neurons. I did this by replacing the last layer from softmax to a sigmoids (so the loss would be sigmoid cross-entropy instead of softmax cross-entropy, and the output would not be a categorical probability distribution anymore, but honestly it didn't make much difference performance-wise in my case), then express the "default" class as 1 minus the maximum activation value from every other class. So, if no class had an activation of 0.5 of greater (i.e. 50% estimated probability of being that class), the "default" class would be the highest scoring one. You can explore this an other similar schemes.

Upvotes: 12

gdelab
gdelab

Reputation: 6220

You should just add images to your dataset that are neither dogs nor cats, label them as "Other", and treat "Other" as normal class in all your code. In particular you'll get a softmax over 3 classes.

The images you're using can be anything (except cats and dogs of course), but should be of the same kind as the ones you'll probably be testing against when using your network. So for instance if you know you'll be testing on images of dogs, cats, and other animals, train with other animals, not with pictures of flowers. If you don't know what you'll be testing with, try to get very varied images, from different sources, etc, so that the network learns well that this class is "anything but cats and dogs" (the wide range of images in the real world that fall in this category should be reflected in your training dataset).

Upvotes: 2

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