I was delighted to have our paper “Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection” accepted in the AccML 2022 workshop at the HiPEAC 2022 conference, where I was first author. You can view the paper on arXiv here. I presented a 20 minute presentation on the paper in-person.

The paper came from our successful completion of the AIMDDE project, where we developed an AI-based solution to industrial defect detection. We were a small team, with one developer (me) and one project lead, thus we had to be as efficient as possible to achieve our goals. I exploited the Bonseyes toolchain and systems such as Docker and high-level domain-specific ML libraries to be highly productive, as well as reproducible. The paper evangelises the value of these workflows for research and SMEs, by using our experience with AIMDDE as a case-study.

For more details, please check out the full paper!