I wrote a simple mobile app so that I can move around and do inferencing on the go. I’ve realized my model have a tendency to simply pick up on the color and the basic shape (blue-round and red-round objects) so my thinking was to find miss-classified examples and place them all in an other-class. My two objects have features besides the color that my model is failing to pick up on. Dataset has reached 10.000 with about 50/50 for my two objects.
The OOD keyword was really valuable!
Found an article where they mention “…we propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.”
So instead of generating these examples I will go around and hunt for them with my cam. I have added a label button so that I can upload them straight to my dataset. Will give that a try atleast.