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Deep Learning Benchmarks and Datasets for Social Media21.html
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<title>Benchmark Datasets for Machine Learning for Natural Disasters</title>
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<h3><b>Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response</b></h3> <!-- Title -->
<h5> Image | General | Response </h5> <!-- Type | Topic | Disaster phase -->
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<div class="w3-container"> <!-- Data description -->
<p> This dataset is a combination of DAD, CrisisMMD, AIDR-Info and DMD data for general-disaster informativeness classification. It contains 59,716 images and is introduced for binary classification.</p>
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<li class="list-group-item"><strong>ML task type:</strong> Binary classification</li>
<li class="list-group-item"><strong>Data Source:</strong> Social Media (Twitter, Google, Instagram)</li>
<li class="list-group-item"><strong>Size:</strong> 59,716 Images </li>
<li class="list-group-item"><strong>Timespan:</strong> N/A </li>
<li class="list-group-item"> <strong>Geographical Coverage:</strong> Global </li>
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<li class="list-group-item"> <strong>Baseline Information</strong></li>
<li class="list-group-item"> <strong>Evaluated on:</strong> ResNet18, ResNet50, ResNet101,
AlexNet, VGG16, DenseNet, SqueezeNet, InceptionNet, MobileNet, EfficientNet </li>
<li class="list-group-item"> <strong>Metrics used:</strong> F1-score</li>
<li class="list-group-item"> <strong>Results as reported in original paper: </strong> F1-score: 0.863 (EfficientNet)</li>
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<li class="list-group-item"> <strong>Find out more by:</strong></li>
<li class="list-group-item"> <a href="https://ieeexplore.ieee.org/document/9381294" target="_blank">Reading the research paper</a></li>
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Firoj Alam, Ferda Ofli, Muhammad Imran, Tanvirul Alam, and Umair Qazi. Deep learning benchmarks and datasets
for social media image classification for disaster response. In 2020 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining, pages 151–158. IEEE, 2020.
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