I’m training a network to do image classification on zoo animals.
I’m a software engineer and not an ML expert, so I’ve been retraining Google’s Inception model and the latest models is trained using Google AutoML Vision.
The network performs really well, but I have trouble with images of animals that I don’t want any labels for. Basically I would like images of those animals to be classified as unknowns or achieve low scores.
I do have images of the animals that I don’t want labels for and I tried putting them all into one “nothing” label together with images I’ve collected of the animals habitats without any animals. This doesn’t really yield any good results though. The network performs for the labeled animals but ends up assigning one of those labels to the other animals as well. Usually with a really high score as well.
I have 14 labels and 10.000 images. I should also mention that the “nothing” label ends up having a lot of images compared to the actual labels. Those images are not included in the 10.000.
Is there any tricks to achieve better results with this? Should I create multiple labels for the images in the “nothing” category maybe?