Bird species recognition using transfer learning with a hybrid hyperparameter optimization scheme (HHOS)

Abstract

The use of automatic bird species recognition methods reduces the burden on scientists, ornithologists, and bird watchers as these methods help identify birds with minimal human effort and intervention. The current study employs a transfer learning approach combined with a Hybrid hyperparameter Optimization Scheme (HHOS) to enhance the efficiency and accuracy of automatic bird species recognition. First, the weights of selected pre-trained deep learning models are downloaded from ImageNet, and a few new trainable layers are added at the top. Thereafter, the selected models are trained using HHOS, which strategically integrates both manual and random searches to achieve favorable results. The manual search relies on domain knowledge and experience to identify the best hyperparameter settings, thereby making the search space smaller and more focused. Random search tests various combinations of the hyperparameters identified in manual search and trains the selected models to achieve the maximum possible accuracy through multiple iterations. Experimental analysis revealed that the Fine-tuned EfficientNetB0 model exhibited superior performance, achieving an accuracy of 99.12%. In contrast, the performance of the ResNet18 model was disappointing with an accuracy of 93.24%, while other models outperformed it.

Topics & Concepts