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Datasets in the computer vision academic research community are primarilystatic. Once a dataset is accepted as a benchmark for a computer vision task,researchers working on this task will not alter it in order to make theirresults reproducible. At the same time, when exploring new tasks and newapplications, datasets tend to be an ever changing entity. A practitioner maycombine existing public datasets, filter images or objects in them, changeannotations or add new ones to fit a task at hand, visualize sample images, orperhaps output statistics in the form of text or plots. In fact, datasetschange as practitioners experiment with data as much as with algorithms, tryingto make the most out of machine learning models. Given that ML and deeplearning call for large volumes of data to produce satisfactory results, it isno surprise that the resulting data and software management associated todealing with live datasets can be quite complex. As far as we know, there is noflexible, publicly available instrument to facilitate manipulating image dataand their annotations throughout a ML pipeline. In this work, we presentShuffler, an open source tool that makes it easy to manage large computervision datasets. It stores annotations in a relational, human-readabledatabase. Shuffler defines over 40 data handling operations with annotationsthat are commonly useful in supervised learning applied to computer vision andsupports some of the most well-known computer vision datasets. Finally, it iseasily extensible, making the addition of new operations and datasets a taskthat is fast and easy to accomplish.

Shuffler: A Large Scale Data Management Tool for ML in Computer Vision