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I have a dataset of images of birds and want to build a CNN classifier that outputs the probability that the fed image(test) is a bird, So that I can accept the image to be a bird beyond a certain threshold.

How should I go about training this ?

One possible way could be to have a second class of all the images/{birds} and then train a two class CNN. But this becomes a very computationally expensive task.

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One solution for images that have a defined bounding box of the bird, would be to use a sliding window and an approach similar to OverFeat http://arxiv.org/abs/1312.6229.

Otherwise, you will have to train against two classes, as you well said. For ease of speed I would suggest the following:

  • Use a GPU accelerated framework (caffe, torch, theano etc)
  • You can take a pretrained network's weights (OverFeat, OxfordNet, AlexNet etc.) and just replace and retrain the top layers.
  • Use an efficient SGD method such as Adagrad, Adadelta, rmsprop etc.
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