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LaboroTomato Dataset

80461
Tagagriculture
Taskinstance segmentation
Release YearMade in 2020
LicenseCC BY-NC-SA 4.0
Download2 GB

Introduction #

Released 2020-07-14 Β·Roman Trigubenko, hfujihara

The Laboro Tomato dataset comprises images capturing tomatoes in various stages of ripening, tailored for tasks involving object detection and instance segmentation. Additionally, the dataset offers two distinct subsets categorized by tomato size. These images were acquired at a local farm, utilizing two separate cameras, each contributing to varying resolutions and image quality.

Each tomato is divided into 2 categories according to size (normal size b_ and cherry tomato l_) and 3 categories depending on the stage of ripening:

  • fully_ripened - complitely red color and ready to be harvested. Filled with red color on 90% or more
  • half_ripened - greenish and needs time to ripen. Filled with red color on 30-89%
  • green - complitely green/white, sometimes with rare red parts. Filled with red color on 0-30%
image
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Dataset LinkHomepage

Summary #

LaboroTomato: Instance Segmentation Dataset is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the agricultural industry.

The dataset consists of 804 images with 10610 labeled objects belonging to 6 different classes including b_green, l_green, l_fully_ripened, and other: b_half_ripened, l_half_ripened, and b_fully_ripened.

Images in the LaboroTomato dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: Train (643 images) and Test (161 images). The dataset was released in 2020 by the Laboro.AI Inc, Japan.

Here is the visualized example grid with animated annotations:

Explore #

LaboroTomato dataset has 804 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 LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
OpenSample annotation mask from LaboroTomatoSample image from LaboroTomato
πŸ‘€
Have a look at 804 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 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 1-6 of 6
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
b_greenβž”
polygon
430
2090
4.86
7.66%
l_greenβž”
polygon
376
4827
12.84
7.82%
l_fully_ripenedβž”
polygon
335
1355
4.04
4.81%
b_half_ripenedβž”
polygon
323
747
2.31
5.08%
l_half_ripenedβž”
polygon
322
1076
3.34
2.96%
b_fully_ripenedβž”
polygon
271
515
1.9
4.77%

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.

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 1-6 of 6
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
l_green
polygon
4827
0.61%
5.77%
0%
23px
0.57%
990px
24.55%
298px
7.28%
25px
0.83%
1053px
34.82%
b_green
polygon
2090
1.6%
14.14%
0.02%
49px
1.22%
1751px
42.4%
472px
11.52%
40px
1.32%
1511px
48.43%
l_fully_ripened
polygon
1355
1.19%
10.93%
0.01%
43px
1.07%
1495px
37.08%
407px
9.9%
41px
1.31%
1450px
47.95%
l_half_ripened
polygon
1076
0.88%
7.78%
0.01%
49px
1.22%
1126px
27.93%
351px
8.58%
43px
1.42%
1105px
36.54%
b_half_ripened
polygon
747
2.21%
15.09%
0.03%
81px
1.95%
1605px
38.58%
539px
13.29%
66px
2.12%
1680px
53.85%
b_fully_ripened
polygon
515
2.51%
17.28%
0.05%
90px
2.16%
1658px
41.12%
586px
14.54%
101px
2.55%
1656px
54.76%

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 10610 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 10610
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
b_fully_ripened
polygon
IMG_1087.jpg
4032 x 3024
1316px
32.64%
1391px
46%
11.64%
2βž”
b_fully_ripened
polygon
IMG_1087.jpg
4032 x 3024
1250px
31%
1168px
38.62%
9.32%
3βž”
b_green
polygon
IMG_1064.jpg
4032 x 3024
311px
7.71%
223px
7.37%
0.47%
4βž”
b_half_ripened
polygon
IMG_1064.jpg
4032 x 3024
315px
7.81%
355px
11.74%
0.61%
5βž”
b_green
polygon
IMG_1064.jpg
4032 x 3024
193px
4.79%
182px
6.02%
0.22%
6βž”
b_fully_ripened
polygon
IMG_1064.jpg
4032 x 3024
304px
7.54%
323px
10.68%
0.59%
7βž”
b_green
polygon
IMG_1064.jpg
4032 x 3024
389px
9.65%
380px
12.57%
0.87%
8βž”
b_green
polygon
IMG_1064.jpg
4032 x 3024
305px
7.56%
322px
10.65%
0.4%
9βž”
b_half_ripened
polygon
IMG_1064.jpg
4032 x 3024
423px
10.49%
380px
12.57%
0.94%
10βž”
b_green
polygon
IMG_1064.jpg
4032 x 3024
513px
12.72%
462px
15.28%
1.52%

License #

LaboroTomato: Instance Segmentation Dataset is under CC BY-NC-SA 4.0 license.

Source

Citation #

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

@dataset{LaboroTomato,
	author={Roman Trigubenko and hfujihara},
	title={LaboroTomato: Instance Segmentation Dataset},
	year={2020},
	url={https://github.com/laboroai/LaboroTomato#readme}
}

Source

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

@misc{ visualization-tools-for-laboro-tomato-dataset,
  title = { Visualization Tools for LaboroTomato Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/laboro-tomato } },
  url = { https://datasetninja.com/laboro-tomato },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset LaboroTomato 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='LaboroTomato', 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.

The data in original format can be downloaded here.

. . .

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