Abstract
This paper describes Team Hope’s system for the NakbaArchiveClassifier Shared Task at Nakba-NLP 2026. The task focuses on binary classification of social media images into two categories: destruction and not_destruction. We evaluated multiple convolutional neural network architectures using transfer learning, including ResNet34, ResNet50, EfficientNet-B0, and a fine-tuned ResNet34 variant with staged training. All models were initialized with ImageNet pretrained weights and fine-tuned on the provided dataset of 2,001 images. The dataset is moderately imbalanced and contains visually diverse Instagram images depicting intact and damaged infrastructure. Our best-performing model, ResNet34 trained for 25 epochs with Adam optimizer and a learning rate of 1e-4, achieved 81% accuracy on the evaluation platform. We provide a comparative analysis of the tested architectures and discuss the impact of model depth, training duration, and class imbalance. Given the political and ethical sensitivity of the dataset, we also include a discussion of responsible AI considerations and potential limitations. Our findings suggest that moderate-depth architectures can generalize effectively in low-resource, contextually complex visual classification tasks.- Anthology ID:
- 2026.nakbanlp-1.25
- Volume:
[Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026](/volumes/2026.nakbanlp-1/)- Month:
- May
- Year:
- 2026
- Address:
- Palma, Mallorca (Spain)
- Editors:
Mustafa Jarrar,Mo El-Haj,Amal Haddad,Serin Atiani,Shadi Abudalfa,Terry Regier,Paul Rayson,Khalil Sima’an,Camille Mansour- Venues:
[NakbaNLP](/venues/nakbanlp/)|[WS](/venues/ws/)- SIG:
- Publisher:
- ELRA Language Resources Association (ELRA)
- Note:
- Pages:
- 187–190
- Language:
- External URL:
[https://lrec.elra.info/lrec2026-ws-nakbanlp-25](https://lrec.elra.info/lrec2026-ws-nakbanlp-25)- DOI:
[10.63317/233j9kgmifw4](https://doi.org/10.63317/233j9kgmifw4)- Cite (ACL):
- Lojien AlKhidir and HebaTalla Abdelhady. 2026. "Hope" at NakbaArchiveClassifier Shared Task: Transfer Learning-Based CNN Models for Infrastructure Damage Detection. InProceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026, pages 187–190, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA). - Cite (Informal): “Hope” at NakbaArchiveClassifier Shared Task: Transfer Learning-Based CNN Models for Infrastructure Damage Detection(AlKhidir & Abdelhady, NakbaNLP 2026)