Hyper_Kvasir_Seg Computer Vision Project
Updated a year ago
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Hyper_Kvasir dataset segmented image part, annotated in YOLO format. Suitable to train object detection models. if you use this dataset than cite the orignal paper of this dataset
@article{Borgli2020, author = { Borgli, Hanna and Thambawita, Vajira and Smedsrud, Pia H and Hicks, Steven and Jha, Debesh and Eskeland, Sigrun L and Randel, Kristin Ranheim and Pogorelov, Konstantin and Lux, Mathias and Nguyen, Duc Tien Dang and Johansen, Dag and Griwodz, Carsten and Stensland, H{\aa}kon K and Garcia-Ceja, Enrique and Schmidt, Peter T and Hammer, Hugo L and Riegler, Michael A and Halvorsen, P{\aa}l and de Lange, Thomas }, doi = {10.1038/s41597-020-00622-y}, issn = {2052-4463}, journal = {Scientific Data}, number = {1}, pages = {283}, title = {{HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy}}, url = {https://doi.org/10.1038/s41597-020-00622-y}, volume = {7}, year = {2020} }
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
hyper_kvasir_seg_dataset,
title = { Hyper_Kvasir_Seg Dataset },
type = { Open Source Dataset },
author = { Zahid },
howpublished = { \url{ https://universe.roboflow.com/zahid-vwxax/hyper_kvasir_seg } },
url = { https://universe.roboflow.com/zahid-vwxax/hyper_kvasir_seg },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { oct },
note = { visited on 2025-03-13 },
}