Dataset Ninja LogoDataset Ninja:

SUIM Dataset

155081
Tagenvironmental, robotics, biology
Tasksemantic segmentation
Release YearMade in 2020
LicenseMIT
Download158 MB

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

ExpandExpand
Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHubDataset LinkKaggleDataset LinkProject Page

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.

OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
OpenSample annotation mask from SUIMSample image from SUIM
👀
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.

Search
Rows 1-8 of 8
Class
Images
Objects
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 5000
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
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 = { jun },
  note = { visited on 2024-06-25 },
}

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.

. . .

Disclaimer #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.