README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - underwater
  - instance
  - segmentation
  - image
  - text
  - salient
size_categories:
  - 10K<n<100K
task_categories:
  - image-segmentation
  - object-detection
  - image-classification

This dataset is proposed by the ICML 2024 paper "Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset", specific parameters about the dataset can be viewed in the paper

In this paper, we construct the first large-scale USIS10K dataset for the underwater salient instance segmentation task, which contains 10,632 images and pixel-level annotations of 7 categories. As far as we know, this is the largest salient instance segmentation dataset available that simultaneously includes Class-Agnostic and Multi-Class labels.

Datasets

The dataset in USIS10K.zip follows the COCO format and is organized as follows:

data
  ├── USIS10K
  |   ├── foreground_annotations
  │   │   ├── foreground_train_annotations.json
  │   │   ├── foreground_val_annotations.json
  │   │   ├── foreground_test_annotations.json
  │   ├── multi_class_annotations
  │   │   ├── multi_class_train_annotations.json
  │   │   ├── multi_class_val_annotations.json
  │   │   ├── multi_class_test_annotations.json
  │   ├── train
  │   │   ├── train_00001.jpg
  │   │   ├── ...
  │   ├── val
  │   │   ├── val_00001.jpg
  │   │   ├── ...
  │   ├── test
  │   │   ├── test_00001.jpg
  │   │   ├── ...

Citation

If you find our repo or USIS10K dataset useful for your research, please cite us:

@inproceedings{lian2024diving,
  title     = {Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset},
  author    = {Lian, Shijie and Zhang, Ziyi and Li, Hua and Li, Wenjie and Yang, Laurence Tianruo and Kwong, Sam and Cong, Runmin},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning},
  pages     = {29545--29559},
  year      = {2024}
  url       = {https://proceedings.mlr.press/v235/lian24c.html},
}