Classifying bird species poses a significant challenge in computer vision, with applications spanning ecology, conservation, and biodiversity monitoring. Our approach to bird species categorization integrates adversarial feature selection, logistic regression, and the Modulo SSD with CascadedVGG16 architecture. Initially, adversarial feature selection using logistic regression extracts robust and discriminative picture characteristics tailored to the unique properties of bird photos. This method effectively identifies essential features resilient against variations in size, rotation, and lighting, crucial for bird species classification.To enhance classification accuracy and efficiency, we employ the Modulo SSD with CascadedVGG16 architecture in the pipeline. Recognizing the prevalence of periodic or circular patterns in bird photos, such as feathers or nests, this architecture combines single-shot detection (SSD) and modulo arithmetic for effective management. The feature extraction process relies on the CascadedVGG16 network, collecting abstract semantic information from bird photos.Our method is rigorously tested on a diverse set of bird species photos, encompassing various visual traits. The experiments demonstrate the effectiveness of our approach in accurately classifying bird species and efficiently extracting their features. The integration of adversarial feature selection, logistic regression, and Modulo SSD with CascadedVGG16 architecture proves instrumental in achieving both accuracy and efficiency in feature extraction and classification for bird species. This research contributes to the field of performability engineering, offering a robust solution for real-world applications in ecology, conservation, and biodiversity monitoring