Multiclass Bird Species Classification Using VGG16 and TensorFlow: A Deep Learning Approach

Classification of birds is of significant importance when it comes to assessment of biological diversity, or the identification of ecosystems that are most threatened. The conventional approaches to identification of species are usually very cumbersome, time consuming and are likely to contain errors since they involve experts. Consequently, this paper represents a significant challenge that can be solved by recent advances in deep learning and, more specifically, transfer learning. Building upon the VGG16 model originally developed for ImageNet, this research fine-tunes this model for bird species classification of twenty types. Specifically, transfer learning enables the model to take some features from the ImageNet dataset, thus reducing the amount of data that a model needs for training while simultaneously trapping the degree of accuracy. To control this, some of the common methods used in data augmentation were used such as random transformations. The strategies used in the increase the model's generalization ability and makes the model immune against unseen data. The proposed model obtained 94% accuracy for training dataset and 91% accuracy for test dataset for species classification. In summary, the paper demonstrates that fine tuning of pre-trained models, in conjunction with data augmentation approach, increases the classification accuracy of complicated data-related tasks such as species identification, thus making it an efficient tool for automation.