OHEM
Online Hard Example Mining
Some object detection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more
effective and efficient. **OHEM**, or **Online Hard Example Mining**, is a bootstrapping technique that modifies [SGD](https://paperswithcode.com/method/sgd) to sample from examples in a non-uniform way depending on the current loss of each example under consideration. The method takes advantage of detection-specific problem structure in which each SGD mini-batch consists of only one or two images, but thousands of candidate examples. The candidate examples are subsampled according to a distribution
that favors diverse, high loss instances.
Training Region-based Object Detectors with Online Hard Example Mining
https://paperswithcode.com/paper/training-region-based-object-detectors-with
Training Region-based Object Detectors with Online Hard Example Mining