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Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained Devices
[article]
2021
arXiv
pre-print
This work presents twelve fine-tuned deep learning architectures to solve the bacterial classification problem over the Digital Image of Bacterial Species Dataset. The base architectures were mainly published as mobile or efficient solutions to the ImageNet challenge, and all experiments presented in this work consisted of making several modifications to the original designs, in order to make them able to solve the bacterial classification problem by using fine-tuning and transfer learning
arXiv:2106.06505v1
fatcat:lnk42blz3rhaxaoihmzwpp2mmu