Bird image classification based on joint, the inception V3 model, and transfer learning

Zelong Tan

doi:10.1117/12.3026684

15 March 2024 Bird image classification based on joint, the inception V3 model, and transfer learning

Zelong Tan

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751N (2024) https://doi.org/10.1117/12.3026684
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

Bird classification is essential in zoology, computer vision, and artificial intelligence. This paper combines the Inception V3 model with transfer learning to create an effective method for classifying bird images. In addition, this paper observes how the model is affected by the number of epochs. Some top convolutional layers are fine-tuned to perform better after training the fully connected layers. Meanwhile, training the model with more epochs to observe the influence of epochs on model loss and accuracy. This research is conducted on the "BIRDS 525 SPECIES" dataset, and the results indicate that the new model achieves good classification results with relatively few epochs. Fine-tuning the model further enhances its generalization ability, and it is observed that due to maintaining most weights unchanged in transfer learning, more epochs cause the model to converge without a decrease in accuracy, effectively addressing overfitting issues. The combination of transfer learning and convolutional neural networks allows the model to perform well on small datasets at a lower cost while improving overfitting issues, making it a promising approach for future applications on various platforms and small-scale devices.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zelong Tan "Bird image classification based on joint, the inception V3 model, and transfer learning", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751N (15 March 2024); https://doi.org/10.1117/12.3026684

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KEYWORDS

Machine learning

Education and training

Data modeling

RGB color model

Image classification

Feature extraction

Overfitting

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