CODEWITHSUNDEEP
pythontensorflowkeras
Nazar Tropanets
Nazar Tropanets

Reputation: 3

CNN on python with Keras

I made a simple CNN that classifies dogs and cats and I want this CNN to detect images that aren't cats or dogs and this must be a third different class. How to implement this? should I use R-CNN or something else?

P.S I use Keras for CNN

Upvotes: 0

Views: 163

Answers (3)

anthonius adi
anthonius adi

Reputation: 23

You should train with new_category by add one more category, it contains images that are not in 2 category before. I mean

--cat_dir
  -*.jpg
--dog_dir
  -*.jpg
--not_at_all_dir
  -*.jpg

so.. total categories you will train are 3 categories.
(categories or classes whatever it is)
then add the output of final dense fullyconnected (3 categories)

Upvotes: 0

Fnguyen
Fnguyen

Reputation: 1177

What you want to do is called "transfer learning" using the learned weights of a net to solve a new problem.

Please note that this is very hard and acts under many constraints i.e. using a CNN that can detect cars to detect trucks is simpler than using a CNN trained to detect people to also detect cats.

In any case you would use your pre-trained model, load the weights and continue to train it with new data and examples.

Whether this is faster or indeed even better than simply training a new model on all desired classes depends on the actual implementation and problem.

Tl:Dr

Transfer learning is hard! Unless you know what you are doing or have a specific reason, just train a new model on all classes.

Upvotes: 2

Rishabh Sahrawat
Rishabh Sahrawat

Reputation: 2507

You can train that almost with the same architecture (of course it depends on this architecture, if it is already bad then it will not be useful on more classes too. I would suggest to use the state of the art model architecture for dogs and cats classification) but you will also need the dogs and cats dataset in addition to this third class dataset. Unfortunately, it is not possible to use pre-trained for making predictions between all 3 classes by only training on the third class later.

So, cut to short, you will need to have all three datasets and train the model from scratch if you want to make predictions between these three classes otherwise use the pre-trained and after training it on third class it can predict if some image belongs to this third class of not.

Upvotes: 0

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