  In this paper, we report the performance benchmarking results of deep
learning models on MLCommons' Science cloud-masking benchmark using a
high-performance computing cluster at New York University (NYU): NYU Greene.
MLCommons is a consortium that develops and maintains several scientific
benchmarks that can benefit from developments in AI. We provide a description
of the cloud-masking benchmark task, updated code, and the best model for this
benchmark when using our selected hyperparameter settings. Our benchmarking
results include the highest accuracy achieved on the NYU system as well as the
average time taken for both training and inference on the benchmark across
several runs/seeds. Our code can be found on GitHub. MLCommons team has been
kept informed about our progress and may use the developed code for their
future work.
