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No Overfit at NakbaArchiveClassifier Shared Task: A Swin Transformer-Based System for Destruction Image Classification

Automated destruction identification from visual data plays a critical role in large-scale documentation, humanitarian analysis, and digital archiving of conflict-related events. Within this context, the Nakba-NLP 2026 Workshop introduced a shared task aimed at training and evaluating a binary image classification model to distinguish between destroyed or damaged infrastructure and intact infrastructure. However, the limited dataset size and the visual variability of real-world scenes make this task particularly challenging. This work presents a Swin Transformer–based framework tailored for destruction image classification. The proposed model employs a hierarchical Swin Transformer backbone for robust feature extraction, followed by a multi-layer perceptron classifier for decision-making. To address the limited data issue, transfer learning and a customized training strategy are applied to adapt the model effectively without full end-to-end retraining. Furthermore, a semi-supervised data expansion approach is utilized to enlarge the training set from 1,400 to 10,000 images, improving model generalization and robustness. Experimental results on the official blind test set demonstrate strong performance, achieving an F1-score of 86.55% and an accuracy of 87.81%, ranking 5th in the shared task.