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Machine learning techniques are becoming a fundamental tool for scientificand engineering progress. These techniques are applied in contexts as diverseas astronomy and spam filtering. However, correctly applying these techniquesrequires careful engineering. Much attention has been paid to the technicalpotential; relatively little attention has been paid to the softwareengineering process required to bring research-based machine learningtechniques into practical utility. Technology companies have supported theengineering community through machine learning frameworks such as TensorFLowand PyTorch, but the details of how to engineer complex machine learning modelsin these frameworks have remained hidden.  To promote best practices within the engineering community, academicinstitutions and Google have partnered to launch a Special Interest Group onMachine Learning Models (SIGMODELS) whose goal is to develop exemplaryimplementations of prominent machine learning models in community locationssuch as the TensorFlow Model Garden (TFMG). The purpose of this report is todefine a process for reproducing a state-of-the-art machine learning model at alevel of quality suitable for inclusion in the TFMG. We define the engineeringprocess and elaborate on each step, from paper analysis to model release. Wereport on our experiences implementing the YOLO model family with a team of 26student researchers, share the tools we developed, and describe the lessons welearned along the way.

An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors