Numerous bird species have become extinct because of anthropogenic activities and climate change. The destruction of habitats at a rapid pace is a significant threat to biodiversity worldwide. Thus, monitoring the distribution of species and identifying the elements that make up the biodiversity of a region are essential for designing conservation stratagems. However, identifying bird species from images is a complicated and tedious task owing to interclass similarities and fine-grained features. To overcome this, in this project, we developed a deep learning-based method using mobile net algorithm to detect and classify bird species endemic to Taiwan and to distinguish them from other object domains. Furthermore, to validate the reliability of the model, we adopted a technique that involves swapping misclassified data between training and validation datasets. The swapped data are retrained until the most suitable result is obtained. Additionally, fivefold cross-validation was performed to verify the predictive performance of the model.