Performance Evaluation of Pre-Trained Deep Learning Models for Bird Species Identification

The goal of this work was to measure the performance of pre-trained deep-learning models for bird species identification. A flatten layer, three dense layers, two dropout layers, and an output layer with a softmax activation function were utilized in the study's CNN classification models. Precision, recall, and f1-score were some of the evaluation measures used to gauge how well the pre-trained models performed. The results show that the ResNet50v2 and Inceptionv3 models successfully identified bird species with high levels of accuracy, with ResNet50v2 achieving an accuracy of 97.32%. VGG19, on the other hand, has a comparably lower accuracy of 67.82%. These results reveal that pre-trained models have great promise to be effective tools for identifying bird species, with ResNet50v2 and Inceptionv3 providing the most promising results.