Probability for correct Image Classification in Tensorflow
Single label classification is not something Neural Networks can do "off-the-shelf". 
How do you train it ? With only data relevant to your target domain ? Your model will only learn to output one.
You have two strategies:

you use the same strategy as in the "HotDog or Not HotDog app", you put the whole imagenet in two different folders, one with the class you want, the other one containing everything else.
You use the convnet as feature extractor and then use a second model like a One-Class SVM.

You have to understand that doing one class classification is not a simple and direct problem like binary classification could be.
I am using Tensorflow retraining model for Image Classification. I am doing single label classification. 
I want to set a threshold for correct classification. 
In other words, if the highest probability is less than a given threshold, I can say that the image is "unknown" i.e. if np.max(results) < 0.5 -> set label as "unknown". 
So, is there any industry standard to set this threshold. I can set a random value say 60%, but is there any literature to back this threshold ?
Any links or references will be very helpful. 
Thanks a lot.