Article
Version 1
Preserved in Portico This version is not peer-reviewed
Modified Deep Neural Networks for Dog Breeds Identification
Version 1
: Received: 17 December 2018 / Approved: 19 December 2018 / Online: 19 December 2018 (07:57:03 CET)
How to cite: Ayanzadeh, A.; Vahidnia, S. Modified Deep Neural Networks for Dog Breeds Identification. Preprints 2018, 2018120232. https://doi.org/10.20944/preprints201812.0232.v1 Ayanzadeh, A.; Vahidnia, S. Modified Deep Neural Networks for Dog Breeds Identification. Preprints 2018, 2018120232. https://doi.org/10.20944/preprints201812.0232.v1
Abstract
In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows the superior performance of proposed method to the previous works on Stanford dog breeds datasets.
Supplementary and Associated Material
Keywords
Computer vision, Data Augmentation, Fine- Tuning, Imagenet
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment