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| 001 | 890968 | ||
| 005 | 20230111074306.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS39084.2020.9324237 |2 doi |
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| 100 | 1 | _ | |a Sedona, Rocco |0 P:(DE-Juel1)178695 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 2020 IEEE International Geoscience and Remote Sensing Symposium |g IGARSS 2020 |c Online event |d 2020-09-26 - 2020-10-02 |w Hawaii |
| 245 | _ | _ | |a Scaling Up a Multispectral Resnet-50 to 128 GPUs |
| 260 | _ | _ | |c 2020 |b IEEE |
| 300 | _ | _ | |a 1058 - 1061 |
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| 520 | _ | _ | |a Similarly to other scientific domains, Deep Learning (DL)holds great promises to fulfil the challenging needs of RemoteSensing (RS) applications. However, the increase in volume,variety and complexity of acquisitions that are carried outon a daily basis by Earth Observation (EO) missions generatesnew processing and storage challenges within operationalprocessing pipelines. The aim of this work is to show thatHigh-Performance Computing (HPC) systems can speed upthe training time of Convolutional Neural Networks (CNNs).Particular attention is put on the monitoring of the classificationaccuracy that usually degrades when using large batchsizes. The experimental results of this work show that thetraining of the model scales up to a batch size of 8,000, obtainingclassification performances in terms of accuracy in linewith those using smaller batch sizes. |
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| 773 | _ | _ | |a 10.1109/IGARSS39084.2020.9324237 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/890968/files/Sedona_Rocco_IGARSS_2020.pdf |y OpenAccess |
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