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mlenthusiast at NakbaArchiveClassifier Shared Task: A Lightweight SVM-Gated Ensemble of EfficientNets for Image Classification

Image classification under strict time constraints requires a delicate balance between feature complexity and computational overhead. This paper presents an optimized ensemble methodology developed for the NAKABA competition, focusing on identifying structural destruction. We propose a hybrid architecture that leverages two distinct Convolutional Neural Networks (EfficientNetB0 and EfficientNetB3) as base feature extractors, coupled with a Support Vector Machine (SVM) functioning as a meta-classifier. Instead of standard probability averaging or processing high-dimensional embeddings directly, the Meta-SVM acts as a learned gating mechanism to optimally combine the low-dimensional probability predictions of the base models. This ensures robust performance without the latency of heavier deep learning architectures. Empirical results demonstrate the efficacy of this approach. The model achieved a validation accuracy of 0.884 and a weighted F1-score of 0.885, with a notable F1-score of 0.839 on the challenging ’destruction’ class. On the official NAKABA leaderboard test set, the ensemble maintained strong generalization, achieving an F1-score of 0.831 and an accuracy of 0.845, which secured the 12th position overall and proved the model’s high effectiveness within the competition’s strict operational constraints.