KvochurHegel at NakbaArchiveClassifier Shared Task: Nakba Image Classification via ConvNeXt-V2 and Label Smoothing

Abstract

This paper presents the KvochurHegel team’s submission to the Nakba Image Classification shared task at the Nakba-NLP 2026 Workshop. The task requires the binary classification of social media images into destruction and not_destruction categories. Given a limited and imbalanced training set of 1,400 images, we utilized a ConvNeXt-V2 Nano backbone combined with extensive data augmentation and label smoothing, prioritizing standard regularization over task-specific architectural modifications. For inference, we applied a 6-view Test-Time Augmentation (TTA) strategy using a hard-voting mechanism. The baseline system achieved a Macro F1-score of 0.8593 and an Accuracy of 0.8706 on the official private test set, ranking 6th out of 16 participating teams.

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