SUIM - Dataset Ninja

Introduction #

Released 2020-04-03 ·Md Jahidul Islam, Chelsey Edge, Yuyang Xiaoet al.

The large-scale annotated SUIM: Semantic Segmentation of Underwater Imagery dataset has a general-purpose application in robotics. It contains over 1500 images with pixel annotations for eight object categories: waterbody_background (BW), human_divers (HD), aquatic_plants_and sea-grass, wrecks_and_ruins (WR), robots (RO), reefs_and_invertebrates (RI), fish_and_vertebrates (FV), sea-floor_and_rocks (SR). The authors use 3-bit binary RGB colors to represent these eight object categories in the image space.

According to the authors, for visually-guided underwater robots, the existing solutions for semantic segmentation and scene parsing are significantly less advanced. The practicalities and limitations are twofold. The existing large-scale annotated data and relevant methodologies are tied to specific applications such as coral reef classification and coverage estimation, fish detection, and segmentation, etc. Other datasets contain either binary annotations for salient foreground pixels or semantic labels for very few object categories (e.g., seagrass, rocks/sand, etc.).

The SUIM dataset has 1525 RGB images for training and validation; another 110 test images are provided for benchmark evaluation of semantic segmentation models. The images are of various spatial resolutions, e.g., 1906×1080, 1280×720, 640×480, and 256×256. These images are carefully chosen from a large pool of samples collected during oceanic explorations and human-robot cooperative experiments in several locations of various water types. We also utilize a few images from large-scale datasets named EUVP, USR-248, and UFO-120, which authors previously proposed for underwater image enhancement and super-resolution problems. The images are chosen to accommodate a diverse set of natural underwater scenes and various setups for human-robot collaborative experiments.

Fig

This figure demonstrates the population of each object category, their pairwise correlations, and the distributions of RGB channel intensity values in the SUIM dataset.

All images of the SUIM dataset are pixel-annotated by seven human participants. Authors followed the guidelines discussed in The Ocean Animal Encyclopedia and “Marine Species Identification Portal for classifying potentially confusing objects of interest such as plants/reefs, vertebrates/invertebrates, etc.

Please note, that some masks include bad data. Check full list at GitHub page

Expand

Summary #

SUIM: Semantic Segmentation of Underwater Imagery is a dataset for a semantic segmentation task. It is used in the environmental research, and in the robotics industry.

The dataset consists of 1550 images with 5000 labeled objects belonging to 8 different classes including waterbody_background, fish_and_vertebrates, reefs_and_invertebrates, and other: sea-floor_and_rocks, human_divers, wrecks_and_ruins, aquatic_plants_and sea-grass, and robots.

Images in the SUIM dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train_val (1440 images) and test (110 images). The dataset was released in 2020 by the University of Minnesota.

Here are the visualized examples for the classes:

Explore #

SUIM dataset has 1550 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

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Have a look at 1550 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 8 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-8 of 8

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
waterbody_background➔ mask	1288	1288	1	37.54%
fish_and_vertebrates➔ mask	1030	1030	1	11.79%
reefs_and_invertebrates➔ mask	1028	1028	1	52.31%
sea-floor_and_rocks➔ mask	635	635	1	34.77%
human_divers➔ mask	405	405	1	7.54%
wrecks_and_ruins➔ mask	275	275	1	41.85%
aquatic_plants_and sea-grass➔ mask	239	239	1	15.12%
robots➔ mask	100	100	1	4.74%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-8 of 8

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
waterbody_background mask	1288	37.54%	98.22%	0%	1px	0.21%	1080px	100%	365px	72.94%	1px	0.16%	1906px	100%
fish_and_vertebrates mask	1030	11.79%	85.29%	0.02%	8px	1.48%	720px	100%	298px	60.87%	8px	1.25%	1280px	100%
reefs_and_invertebrates mask	1028	52.31%	98.05%	0%	5px	1.04%	1080px	100%	408px	82.62%	1px	0.16%	1906px	100%
sea-floor_and_rocks mask	635	34.77%	97.81%	0.02%	12px	2.5%	1072px	100%	332px	66.85%	50px	7.81%	1906px	100%
human_divers mask	405	7.54%	45.36%	0.19%	23px	4.79%	772px	100%	240px	45.57%	14px	2.19%	1220px	100%
wrecks_and_ruins mask	275	41.85%	94.73%	1.77%	58px	12.08%	720px	100%	411px	84.13%	108px	16.88%	1280px	100%
aquatic_plants_and sea-grass mask	239	15.12%	81.45%	0.01%	5px	1.04%	1032px	100%	282px	56.51%	9px	1.41%	1422px	100%
robots mask	100	4.74%	28.74%	0.04%	20px	3.7%	720px	100%	195px	30.29%	21px	2.19%	831px	94.25%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 5000 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 5000

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	waterbody_background mask	w_r_30_.jpg	480 x 640	358px	74.58%	640px	100%	48.86%
2➔	wrecks_and_ruins mask	w_r_30_.jpg	480 x 640	480px	100%	499px	77.97%	26.45%
3➔	fish_and_vertebrates mask	w_r_30_.jpg	480 x 640	154px	32.08%	238px	37.19%	4.91%
4➔	sea-floor_and_rocks mask	w_r_30_.jpg	480 x 640	149px	31.04%	640px	100%	19.78%
5➔	waterbody_background mask	d_r_386_.jpg	540 x 960	321px	59.44%	960px	100%	33.49%
6➔	human_divers mask	d_r_386_.jpg	540 x 960	315px	58.33%	449px	46.77%	15.63%
7➔	robots mask	d_r_386_.jpg	540 x 960	93px	17.22%	229px	23.85%	1.84%
8➔	sea-floor_and_rocks mask	d_r_386_.jpg	540 x 960	304px	56.3%	960px	100%	49.03%
9➔	waterbody_background mask	f_r_1059_.jpg	480 x 640	367px	76.46%	640px	100%	53.5%
10➔	human_divers mask	f_r_1059_.jpg	480 x 640	65px	13.54%	118px	18.44%	0.84%

License #

SUIM: Semantic Segmentation of Underwater Imagery is under MIT license.

Citation #

If you make use of the SUIM data, please cite the following reference:

@inproceedings{islam2020suim,
  title={{Semantic Segmentation of Underwater Imagery: Dataset and Benchmark}},
  author={Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz, Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  organization={IEEE/RSJ}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-suim-dataset,
  title = { Visualization Tools for SUIM Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/suim } },
  url = { https://datasetninja.com/suim },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset SUIM can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='SUIM', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

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Disclaimer #

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