Abstract
This study provides a multi-class fine-grained image identification challenge, specifically identifying the breed of a dog in a given image. The demonstrated system makes use of cutting-edge deep learning techniques, such as convolutional neural networks. The study presents a dog breed identification system that utilizes deep learning and transfer learning to improve the accuracy of identifying different breeds of dogs. The ResNet-50 model, a pre-trained deep convolutional neural network, was used as the base for the model, and transfer learning was applied to fine-tune the model for the specific task of dog breed identification. The results showed that the proposed system achieved high accuracy in identifying dog breeds. Overall, this study demonstrates the effectiveness of using deep learning techniques and pre-trained models with transfer learning for dog breed identification. However, it is important to note that dog breed identification is not always an exact science, and there may be some uncertainty or disagreement among experts. Additionally, mixed-breed dogs may not fit neatly into a single-breed category. This study presents an empirical evaluation of a deep learning-based dog breed identifier. The identifier was trained on a large dataset of dog images, consisting of 120 breeds and 20,580 images. The goal of the identifier is to accurately predict the breed of a dog from an input image.
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Tuteja, A., Bathla, S., Jain, P., Garg, U., Dureja, A., Dureja, A. (2024). Dog Breed Identification Using Deep Learning. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 785. Springer, Singapore. https://doi.org/10.1007/978-981-99-6544-1_39
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