Size Does Matter: Overcoming Limitations during Training when using a Feature Pyramid Network

Abstract

State-of-the-art object detectors need to be trained with a wide variety of data in order to perform well in real-world problems. Training-data-diversity is very important to achieve good generalization. However, there are scenarios where we have training data with certain limitations. One such scenario is when the objects of the testing set have a different size (discrepancy) from the objects used during training. Another scenario is when we have high-resolution images with a dimension that is not supported by the model. To address these problems, we propose a novel pipeline that is able to handle high-resolution images by cropping the original image into sub-images and put them back in the end. Also, in the case of the discrepancy of object sizes, we propose two different techniques based on scaling the image up and down in order to have an acceptable performance. In addition, we also use the information from the Feature Pyramid Network to remove false-positives. Our proposed methods overcome state-of-the-art data augmentation policies and our models can generalize to different object sizes even though limited data is provided.