# Improvements & Evaluations on the MLCommons CloudMask Benchmark

arXiv: https://arxiv.org/abs/2403.04553 · HTML: https://mlsanity.com/paper/2403.04553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors](https://mlsanity.com/paper/1810.05801/) (2018)
- [MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems](https://mlsanity.com/paper/2110.11466/) (2021)

## Abstract (arXiv)

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.