I've to segment defects from an image. The image consists of only tomatoes with it's defects in it. The defects and tomatoes in the dataset are as follows:
tomato = 20900
tip = 2129 spots holes = 804 cuts cracks = 267 shrivelled = 193 glare = 3485 back tip = 137 stalk = 119 green area = 610
As one can see the data is highly unbalanced. Within a single image of tomatoes we may find some defects and we may not find any. How to do training of such cases of multi-class identification and classification ?
I've tried many standard models in given in tensorflow-object-detection api.It detects
glare well as there numbers are higher. Any suggestions ?