Picture Perfect - Hackster.io

As machine learning algorithms continue to advance, the need for good, accurately annotated datasets is becoming increasingly apparent. With less and less room for optimization of the models themselves, more attention is finally being turned to addressing issues with data quality. After all, no matter how much potential a particular model has, that potential cannot be realized without a good dataset to learn from. Image classification is a common task for machine learning models, and these models suffer from a particular type of data problem called co-occurrence bias. Co-occurrence bias can cause irrelevant details to get the attention of a machine learning model, leading to incorrect predictions. For example, if a dataset used to train an object recognition model only contains images of boats in the ocean, the model may start classifying anything related to the ocean, such as beaches or waves, as boats.