Fish Image Classification Using Adaptive Learning Rate  In Transfer Learning Method

Rizka Suhana; Wayan Firdaus Mahmudy; Agung Setia Budi

doi:10.17977/um018v5i12022p67-77

Fish Image Classification Using Adaptive Learning Rate In Transfer Learning Method

Rizka Suhana, Wayan Firdaus Mahmudy, Agung Setia Budi

Abstract

The existence of fish species diversity in coastal ecosystems which include mangrove forests, seagrass beds and coral reefs is one of the benchmarks in determining health in coastal ecosystems. It is certain that we must maintain, preserve and care for so that conservation efforts need to be carried out in water areas. Many experts at the Indonesian Fisheries and Marine Research and Development Agency often classify fish images manually, of course it will take a long time, therefore with today's developments they can use the latest technology. One of the reliable techniques in terms of image classification is Convolutional Neural Network (CNN). As time goes by, of course, many people want fast learning and solving new problems faster and better, so transfer learning appears, which adopts part of CNN, the name is modified convolution layer. Observing the needs of experts in the field of marine conservation, the researchers decided to solve this problem by using transfer learning modifications. The transfer learning used is an architectural model from the pre-trained Mobilenet V2, which is known for its light computing process and can be applied to our gadgets and other embedded tools. The research image data used is 49.281 data of various sizes and there are 18 types of fish, in the pre-processing data there is a resize of the image to a size of 224x224 pixels. testing with the modified transfer learning architectural model obtained an accuracy score of 99.54%, this model is quite reliable in classifying fish images.

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DOI: http://dx.doi.org/10.17977/um018v5i12022p67-77

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