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. Preprints2018, 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
Ayanzadeh, A.; Vahidnia, S. Modified Deep Neural Networks for Dog Breeds Identification. Preprints2018, 2018120232. https://doi.org/10.20944/preprints201812.0232.v1
APA Style
Ayanzadeh, A., & Vahidnia, S. (2018). Modified Deep Neural Networks for Dog Breeds Identification. Preprints. https://doi.org/10.20944/preprints201812.0232.v1
Chicago/Turabian Style
Ayanzadeh, A. and Sahand Vahidnia. 2018 "Modified Deep Neural Networks for Dog Breeds Identification" Preprints. 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.
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.
