Optimizing Precision in High Variability Image Dataset through Adaptive Ensemble of Deep Convolution Neural Network

Image classification in datasets with a lot of variation is hard because of differences within classes and similarities between classes, which can lead to wrong classifications and less accuracy. This research creates an adaptive ensemble framework that combines NASNetLarge and EfficientNetB7, using their unique strengths to improve the accuracy of classification. The proposed hybrid model dynamically adjusts its architecture based on data-driven insights, optimizing feature extraction and representation learning. Individual baseline models are trained and then rigorously compared to the ensemble approach. This shows how adaptive fusion works and what its benefits are. We test our framework on the CUB-200-2011 bird species dataset, which is a standard for fine-grained image classification. In both accuracy and robustness, our method does much better than traditional deep learning architectures. Extensive experimentation involving hyperparameter tuning, ensemble weight optimization, and architectural modifications further refine the model’s efficacy. The results show that the hybrid model can handle problems in image datasets with a lot of variation. This means that it can be used in real life for things like monitoring biodiversity, medical imaging, and remote sensing.