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QCRI/MEDIC

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Use restrictions

The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-sa-4.0, from the tagEvidence: README.md at bc2e6f3
Licence files: licenses_by-nc-sa_4.0_legalcode.txt, detected as cc-by-nc-sa-4.0Evidence: licenses_by-nc-sa_4.0_legalcode.txt at bc2e6f3
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

Each cell links to what was checked: a file, a file list or a Hub record.

Tasks

image-classification

Citation text from the card

@article{alam2022medic,
title={{MEDIC}: A Multi-Task Learning Dataset for Disaster Image Classification},
author={Firoj Alam and Tanvirul Alam and Md. Arid Hasan and Abul Hasnat and Muhammad Imran and Ferda Ofli},
Keywords = {Multi-task Learning, Social media images, Image Classification, Natural disasters, Crisis Informatics, Deep learning, Dataset},
journal={Neural Computing and Applications},
volume={35},
issue={3},
pages={2609--2632},
year={2023},
publisher={Springer}
}

@InProceedings{crisismmd2018icwsm,
  author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad},
  title = {{CrisisMMD}: Multimodal Twitter Datasets from Natural Disasters},
  booktitle = {Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM)},
  year = {2018},
  month = {June},
  date = {23-28},
  location = {USA}
}

@inproceedings{10.1109/ASONAM49781.2020.9381294,
author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad and Alam, Tanvirul and Qazi, Umair},
title = {Deep learning benchmarks and datasets for social media image classification for disaster response},
year = {2021},
isbn = {9781728110561},
publisher = {IEEE Press},
url = {https://doi.org/10.1109/ASONAM49781.2020.9381294},
doi = {10.1109/ASONAM49781.2020.9381294},
booktitle = {Proceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
pages = {151–158},
numpages = {8},
keywords = {benchmarking, crisis computing, deep learning, disaster image classification, n

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dataset_licence({ "id": "QCRI/MEDIC" })
signal: "restrictive"
stated_licence: "cc-by-nc-sa-4.0"
sources: 0