Faisal_Adam at NakbaArchiveClassifier Shared Task: Archival Image Classification for Structural Destruction: A Robust Pipeline Using ResNet-50 and Test-Time Augmentation

Abstract

This paper describes our system submission for the Nakba Archive Image Classification task, which requires predicting the presence of structural destruction in historical archival photographs. We framed this as a binary computer vision classification problem (destruction vs. not_destruction). Our system utilizes a pre-trained ResNet-50 convolutional neural network, adapted for binary output, combined with strategic prediction threshold tuning. Evaluated on the unseen final test set, our model achieved a macro F1-score of 0.450 and a balanced accuracy of 0.527, serving as an exploratory baseline that highlights the unique challenges of processing degraded historical imagery.

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