LiamLian0727/UIIS10K

We propose a large-scale underwater instance segmentation dataset, UIIS10K, which includes 10,048 images with pixel-level annotations for 10 categories. As far as we know, this is the largest underwater instance segmentation dataset available and can be used as a benchmark for evaluating underwater segmentation methods.

More information about this dataset pleaser refer to "Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation".

The dataset in UIIS10K.zip follows the COCO format and is organized as follows:

    data
      ├── UIIS10K
      |   ├── annotations
      │   │   ├── multiclass_train.json
      │   │   ├── multiclass_test.json
      │   ├── img
      │   │   ├── train_00001.jpg
      │   │   ├── ...
      │   │   ├── test_00001.jpg
      │   │   ├── ...

Citation

If you find our repo useful for your research, please cite us:

@InProceedings{UIIS_Dataset_2023,
    author    = {Shijie Lian, Hua Li, Runmin Cong, Suqi Li, Wei Zhang, Sam Kwong},
    title     = {WaterMask: Instance Segmentation for Underwater Imagery},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {1305-1315}
}

@article{UIIS10K_Dataset_2025,
    author    = {Hua Li, Shijie Lian, Zhiyuan Li, Runmin Cong, Chongyi Li, Laurence T. Yang, Weidong Zhang, Sam Kwong},
    title     = {Taming SAM for Underwater Instance Segmentation and Beyond},
    year      = {2025},
    journal   = {arXiv preprint arXiv:2505.15581},
}