{"slug": "hope-at-nakbaarchiveclassifier-shared-task-transfer-learning-based-cnn-models", "title": "\"Hope\" at NakbaArchiveClassifier Shared Task: Transfer Learning-Based CNN Models for Infrastructure Damage Detection", "summary": "Team Hope's ResNet34 model, fine-tuned with ImageNet pretrained weights for 25 epochs using the Adam optimizer and a learning rate of 1e-4, achieved 81% accuracy on the NakbaArchiveClassifier Shared Task at Nakba-NLP 2026, outperforming other tested architectures including ResNet50 and EfficientNet-B0. The system, developed by Lojien AlKhidir and HebaTalla Abdelhady, classified 2,001 Instagram images into destruction and not_destruction categories, demonstrating that moderate-depth CNNs can generalize effectively in low-resource visual classification tasks.", "body_md": "##### Abstract\n\nThis 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:\n- 2026.nakbanlp-1.25\n- Volume:\n[Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026](/volumes/2026.nakbanlp-1/)- Month:\n- May\n- Year:\n- 2026\n- Address:\n- Palma, Mallorca (Spain)\n- Editors:\n[Mustafa Jarrar](/people/mustafa-jarrar/),[Mo El-Haj](/people/mo-el-haj/),[Amal Haddad](/people/amal-haddad/unverified/),[Serin Atiani](/people/serin-atiani/unverified/),[Shadi Abudalfa](/people/shadi-abudalfa/),[Terry Regier](/people/terry-regier/unverified/),[Paul Rayson](/people/paul-rayson/),[Khalil Sima’an](/people/khalil-simaan/),[Camille Mansour](/people/camille-mansour/unverified/)- Venues:\n[NakbaNLP](/venues/nakbanlp/)|[WS](/venues/ws/)- SIG:\n- Publisher:\n- ELRA Language Resources Association (ELRA)\n- Note:\n- Pages:\n- 187–190\n- Language:\n- External URL:\n[https://lrec.elra.info/lrec2026-ws-nakbanlp-25](https://lrec.elra.info/lrec2026-ws-nakbanlp-25)- DOI:\n[10.63317/233j9kgmifw4](https://doi.org/10.63317/233j9kgmifw4)- Cite (ACL):\n- Lojien AlKhidir and HebaTalla Abdelhady. 2026.\n[\"Hope\" at NakbaArchiveClassifier Shared Task: Transfer Learning-Based CNN Models for Infrastructure Damage Detection](https://aclanthology.org/2026.nakbanlp-1.25/). In*Proceedings 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):\n[“Hope” at NakbaArchiveClassifier Shared Task: Transfer Learning-Based CNN Models for Infrastructure Damage Detection](https://aclanthology.org/2026.nakbanlp-1.25/)(AlKhidir & Abdelhady, NakbaNLP 2026)", "url": "https://wpnews.pro/news/hope-at-nakbaarchiveclassifier-shared-task-transfer-learning-based-cnn-models", "canonical_source": "https://aclanthology.org/2026.nakbanlp-1.25/", "published_at": "2026-07-24 00:00:00+00:00", "updated_at": "2026-08-04 21:51:50.224225+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision"], "entities": ["Team Hope", "NakbaArchiveClassifier", "Nakba-NLP 2026", "ResNet34", "ResNet50", "EfficientNet-B0", "Lojien AlKhidir", "HebaTalla Abdelhady"], "alternates": {"html": "https://wpnews.pro/news/hope-at-nakbaarchiveclassifier-shared-task-transfer-learning-based-cnn-models", "markdown": "https://wpnews.pro/news/hope-at-nakbaarchiveclassifier-shared-task-transfer-learning-based-cnn-models.md", "text": "https://wpnews.pro/news/hope-at-nakbaarchiveclassifier-shared-task-transfer-learning-based-cnn-models.txt", "jsonld": "https://wpnews.pro/news/hope-at-nakbaarchiveclassifier-shared-task-transfer-learning-based-cnn-models.jsonld"}}