Multi-Label Classification with Deep Learning and Manual Data Collection for Identifying Similar Bird Species

Abstract

This study delves into the challenge of classifying visually similar bird species, an area of significant interest in the field of fine-grained image classification. Utilizing a substantial dataset comprising images of ten bird species which was selected carefully to challenge the model to classify species of extreme similarities. To achieve this, we were keen to collect the data with subtle visual dissimilarities and of different positions taken for these birds. The research explores the potential of deep learning techniques to differentiate species based on subtle inter-species variations. This task is particularly demanding due to the minimal yet critical differences between these closely related species. Our research leveraged a unique deep learning model using convolutional neural networks (CNNs) to accurately classify birds with minimal visual differences. This innovative approach marks a significant step forward in machine learning for biological classification, with implications for biodiversity and ecological conservation. Our study demonstrates the effectiveness of our deep learning model in accurately classifying bird species, showcasing the potential of advanced techniques in complex Classification tasks. This research enhances the use of computational methods in biodiversity and ecological conservation. Additionally, it underscores the importance of birds as indicators of environmental changes, such as climate shifts, aiding in early detection of potential ecological issues.