MLCommons Science Cloudmask benchmark

The scientific objective of CloudMask is to develop a segmentation model for classifying the pixels in satellite images. This classification allows to determine whether the given pixel belongs to a cloud or to a clear sky. The benchmark can be considered as both training and inference focused, where the science metric is same as the classification accuracy — number of pixels classified correctly. The performance metric, can be inference timing and scalability on the training across a number of GPUs.

Participants and Collaborators


  • Working code that can run benchmarks in parallel
  • Report
  • Submission of the benchmark to MLCommons


Last modified March 31, 2023: add projects and rivanna limitations (fce4926)