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Applying transfer learning for marine animal image classification using pre-trained convolutional neural network [thesis] / Alexander S. Alejado. Ellaine T. Gabotero, and Naomi P. Ezaki.

By: Material type: TextTextPublication details: Dumaguete City : Foundation University, 2019.Description: v, 53 leaves : ill. (col.) 275 cmSubject(s): LOC classification:
  • LG 221 D35 C66  A5 C66 .2019 Al252
Dissertation note: Summary: This study aims to classify each image into its class of marine animals and to determine the accuracy pf the image classifier. The image classifier applies Transfer Learning using a pre-trained Convolutional Neural Network. Transfer learning is using existing labeled data gained from solving one problem and then apply it to a different but related problem. In applying transfer learning, the researchers use a pre-trained Convolutional Neural Network. This machine learning algorithm is used because of its benefit in detecting patterns from images in order to recognize objects. In addition, they eliminate the need for manual feature extraction and they can be trained for new recognition tasks which let the researchers enable the use of pre-trained models.
List(s) this item appears in: Computer Science, Bachelor of Science
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Item type Current library Call number Status Barcode
Thesis Thesis Foundationiana Section, University Records and Archives Center (URAC) Undergraduate Thesis LG 221 D35 C66 A5 C66 2019 Al252 (Browse shelf(Opens below)) Available 0132024005004

Thesis Undergraduate (BS Computer Science) -- Foundation University, 2019.

Includes bibliographical references and appendices.

This study aims to classify each image into its class of marine animals and to determine the accuracy pf the image classifier. The image classifier applies Transfer Learning using a pre-trained Convolutional Neural Network. Transfer learning is using existing labeled data gained from solving one problem and then apply it to a different but related problem. In applying transfer learning, the researchers use a pre-trained Convolutional Neural Network. This machine learning algorithm is used because of its benefit in detecting patterns from images in order to recognize objects. In addition, they eliminate the need for manual feature extraction and they can be trained for new recognition tasks which let the researchers enable the use of pre-trained models.

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