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In this project, we present an Android application that leverages deep learning, specifically Convolutional Neural Networks (CNNs) and transfer learning, to accurately classify dog breeds from images. Our application allows users to upload or capture images of dogs, preprocesses the images, and employs a trained CNN model for breed prediction. We propose two models for dog breed classification, both rooted in deep learning principles and utilizing transfer learning. Transfer learning is a key aspect of our approach, enabling the utilization of pre-trained models on extensive datasets like ImageNet to enhance the performance of breed classification. Additionally, we employ data augmentation techniques to augment the dataset, thereby improving model robustness and generalization. Overall, our work serves as a comprehensive review of methodologies in deep learning-based dog breed classification, in the fields of computer vision and machine learning.
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Cite Article:
"A Deep Learning based approach for dog breed prediction", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.673 - 680, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404093.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator