---
license: apache-2.0
language:
- en
tags:
- underwater 
- instance 
- segmentation
- image
- text
size_categories:
- 1K<n<10K
task_categories:
- image-segmentation
- object-detection
- image-classification
---


This dataset is proposed by the ICCV 2023 paper "[WaterMask: Instance Segmentation for Underwater Imagery](https://github.com/LiamLian0727/WaterMask)", specific parameters about the dataset can be viewed in the paper

The Underwater Image Instance Segmentation (UIIS) dataset contains 4,628 images with pixel-level annotations in seven categories used for the underwater instance segmentation task. The dataset is organized in MS COCO format and the annotation files and images for training and testing are in UDW files.


### Updatae: UIIS10K

[**UIIS10K**](https://huggingface.co/datasets/LiamLian0727/UIIS10K) upgrades the original UIIS dataset with **10 048 underwater images** annotated at **pixel level** for **10 object classes** (fish · reptiles · artiodactyla · mollusks · corals · plants · garbage · ruins · divers · robots).

- **Largest underwater benchmark** – more images and masks than any existing general underwater instance segmentation dataset.  
- **Scene diversity** – shallow & deep water, clear & turbid conditions, multiple resolutions, complex backgrounds, heavy occlusions.  
- **Crowded scenes** – 23 % of images contain ≥ 5 instances.  (up to 80+ per image).  
- **Multi-task ready** – pixel masks + bounding boxes support instance segmentation, object detection, and semantic segmentation.

---

### 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},
    title     = {Taming SAM for Underwater Instance Segmentation and Beyond},
    year      = {2025},
    journal   = {arXiv preprint arXiv:2505.15581},
}
```