I want to use InceptionV3 on one class of image (ie. to detect one type of object/one label only). For example, to detect 'penguin' or 'no penguin'. All my images have been cropped down to mostly display the object, so there is barely any background present and the object is in clear view. I have used bottlenecking; I have frozen the top layer of Inception so that I can retrain Inception to classify my object. However, this means that I have no alternative class to train with since I only want to classify one object.
The issue is that val_accuracy is always equal to 1, implying a perfect model, which my better nature says is too good to be true. Therefore, I believe the problem is that I have only provided the model with 1 class. How can I overcome this?
Finally, I have tested with InceptionV3 and an out-of-the-box model just to check that my Inception set up was not the issue. Both yielded varying training accuracy, but again, the basic model produced val_accuracy equal to 1 every time. I have also experimented with train-test splitting such as 10% to train with and 90% for validation. To my knowledge, a benefit of transfer learning is that you can use small datasets to train with. Even so, my dataset is 3300.