Authors - A. Harshavardhan, Krishna Anirudh Gunturi, Rikhil Rao Janagama, Navaneeth Reddy Nalla, N V Abhijeet Mukund, Avire Kaushik Abstract - The Age classification is a critical task in computer vision with widespread applications in fields such as healthcare, security, and autonomous systems. This project presents a deep learning approach for multi-class image classification using feature extraction with the EfficientNetB3 architecture. The model was trained on a dataset that has images labeled according to different age groups, where the images were preprocessed, normalized, and sized to a steady resolution appropriate for EfficientNetB3 input. Data handling was simplified using pandas and ImageDataGenerator, ensuring proper splitting into training, validation, and test sets, with suitable shuffling and augmentation strategies applied to improve generalization. This model influences EfficientNetB3 as a feature extractor, combined with a custom classification head containing Batch Normalization, L1/L2 regularization with Dense layers, Dropout, and a SoftMax output layer. This model was trained using the Adamax optimizer and categorical cross-entropy loss, with performance monitored through accuracy and loss metrics over multiple epochs. Training history was seen to identify the epochs corresponding to the best validation performance. Assessment of the model on the test data-set includes loss, accuracy, confusion matrix, and a comprehensive classification report with precision, recall, and F1-score for each set. The results demonstrate that transfer learning, combined with careful preprocessing and regularization, can achieve robust performance in image classification tasks. This pipeline provides a producible and scalable framework for multi-class image classification and can be extended to other datasets and real-world applications requiring automatic image recognition.
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