  Building instance detection models that are data efficient and can handle
rare object categories is an important challenge in computer vision. But data
collection methods and metrics are lack of research towards real scenarios
application using neural network. Here, we perform a systematic study of the
Object Occlusion data collection and augmentation methods where we imitate
object occlusion relationship in target scenarios. However, we find that the
simple mechanism of object occlusion is good enough and can provide acceptable
accuracy in real scenarios adding new category. We illustate that only adding
15 images of new category in a half million training dataset with hundreds
categories, can give this new category 95% accuracy in unseen test dataset
including thousands of images of this category.
