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| Hauptseite > Publikationsdatenbank > Accelerating Hyperparameter Tuning of a Deep Learning Model for Remote Sensing Image Classification > print |
| Hauptseite > Publikationsdatenbank > Accelerating Hyperparameter Tuning of a Deep Learning Model for Remote Sensing Image Classification > print |
| 001 | 910149 | ||
| 005 | 20230502100726.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS46834.2022.9883257 |2 doi |
| 024 | 7 | _ | |a 2128/32035 |2 Handle |
| 024 | 7 | _ | |a WOS:000920916600066 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-03638 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Aach, Marcel |0 P:(DE-Juel1)180916 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 2022 IEEE International Geoscience and Remote Sensing Symposium |g IGARSS 2022 |c Kuala Lumpur |d 2022-07-17 - 2022-07-22 |w Malaysia |
| 245 | _ | _ | |a Accelerating Hyperparameter Tuning of a Deep Learning Model for Remote Sensing Image Classification |
| 260 | _ | _ | |c 2022 |b IEEE |
| 300 | _ | _ | |a 263-266 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a Deep Learning models have proven necessary in dealing with the challenges posed by the continuous growth of data volume acquired from satellites and the increasing complexity of new Remote Sensing applications. To obtain the best performance from such models, it is necessary to fine-tune their hyperparameters. Since the models might have massive amounts of parameters that need to be tuned, this process requires many computational resources. In this work, a method to accelerate hyperparameter optimization on a High-Performance Computing system is proposed. The data batch size is increased during the training, leading to a more efficient execution on Graphics Processing Units. The experimental results confirm that this method reduces the runtime of the hyperparameter optimization step by a factor of 3 while achieving the same validation accuracy as a standard training procedure with a fixed batch size. |
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| 773 | _ | _ | |a 10.1109/IGARSS46834.2022.9883257 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/910149/files/IGARSS2022_Aach_et_al.pdf |y OpenAccess |
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