This paper discusses the evaluation of deep learning models on the MLCommons CloudMask Benchmark, focusing on cloud masking in atmospheric sciences. The study presents benchmarking results, model comparisons, and computational performance metrics. Various methods, including rule-based and deep learning techniques, are analyzed for cloud masking tasks using satellite images from Sentinel-3. The study emphasizes accuracy, training time, and inference time as key evaluation criteria. The authors provide insights into the challenges and advancements in cloud masking algorithms using AI technologies.
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by Varshitha Ch... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04553.pdfDeeper Inquiries