Improvements & Evaluations on the MLCommons CloudMask Benchmark
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.
Code (1)
Tasks
BenchmarkingSimilar Papers 제목 키워드 기반
MLHarness: A Scalable Benchmarking System for MLCommons
With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common set of measures for ML/DL models with larg…
BenchmarkingMLCommons Cloud Masking Benchmark with Early Stopping
In this paper, we report on work performed for the MLCommons Science Working Group on the cloud masking benchmark. MLCommons is a consortium that develops and maintains several scientific benchmarks that aim to benefit d…
An MLCommons Scientific Benchmarks Ontology
Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning …
An Overview of MLCommons Cloud Mask Benchmark: Related Research and Data
Cloud masking is a crucial task that is well-motivated for meteorology and its applications in environmental and atmospheric sciences. Its goal is, given satellite images, to accurately generate cloud masks that identify…
MLPerf Automotive
We present MLPerf Automotive, the first standardized public benchmark for evaluating Machine Learning systems that are deployed for AI acceleration in automotive systems. Developed through a collaborative partnership bet…
2D Semantic Segmentation2D Object Detection3D Object Detection