FN Archimer Export Format
PT J
TI EcoTransLearn: an R-package to easily use Transfer Learning for Ecological Studies. A plankton case study
BT 
AF Wacquet, Guillaume
   Lefebvre, Alain
AS 1:1;2:1;
FF 1:PDG-ODE-LITTORAL-LERBL;2:PDG-ODE-LITTORAL-LERBL;
C1 Laboratoire Environnement et Ressources IFREMER (French Research Institute for Exploitation of the Sea), Unité Littoral, , 150 Quai Gambetta, 62200 Boulogne-sur-Mer, France
C2 IFREMER, FRANCE
SI BOULOGNE
SE PDG-ODE-LITTORAL-LERBL
IN WOS Ifremer UPR
IF 5.8
TC 0
UR https://archimer.ifremer.fr/doc/00800/91232/96986.pdf
   https://archimer.ifremer.fr/doc/00800/91232/96987.docx
LA English
DT Article
AB In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training data. To compensate the lack of “easy-to-use” software optimized for ecological studies, we propose the EcoTransLearn R-package, which allows greater automation in classification of images acquired with various devices (FlowCam, ZooScan, photographs, etc.), thanks to different TL methods pre-trained on the generic ImageNet dataset. Availability and Implementation EcoTransLearn is an open-source package. It is implemented in R, and calls Python scripts for image classification step (using reticulate and tensorflow libraries). The source code, instruction manual and examples can be found at https://github.com/IFREMER-LERBL/EcoTransLearn. Supplementary information Supplementary data are available at Bioinformatics online. 
PY 2022
PD DEC
SO Bioinformatics
SN 1367-4803
PU Oxford University Press (OUP)
VL 38
IS 24
UT 000880341400001
BP 5469
EP 5471
DI 10.1093/bioinformatics/btac703
ID 91232
ER

EF