@inproceedings{adam-aliyu-2026-faisal,
title = "{F}aisal{\_}{A}dam at {N}akba{A}rchive{C}lassifier Shared Task: Archival Image Classification for Structural Destruction: A Robust Pipeline Using {R}es{N}et-50 and Test-Time Augmentation",
author = "Adam, Faisal Muhammad and
Aliyu, Salisu",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.11/",
doi = "10.63317/3s6qbjzfgsbh",
pages = "104--107",
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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<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.</abstract>
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%0 Conference Proceedings
%T Faisal_Adam at NakbaArchiveClassifier Shared Task: Archival Image Classification for Structural Destruction: A Robust Pipeline Using ResNet-50 and Test-Time Augmentation
%A Adam, Faisal Muhammad
%A Aliyu, Salisu
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F adam-aliyu-2026-faisal
%X 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.
%R 10.63317/3s6qbjzfgsbh
%U https://aclanthology.org/2026.nakbanlp-1.11/
%U https://doi.org/10.63317/3s6qbjzfgsbh
%P 104-107
Markdown (Informal)
[Faisal_Adam at NakbaArchiveClassifier Shared Task: Archival Image Classification for Structural Destruction: A Robust Pipeline Using ResNet-50 and Test-Time Augmentation](https://aclanthology.org/2026.nakbanlp-1.11/) (Adam & Aliyu, NakbaNLP 2026)
ACL