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Sweet Pepper Dataset

62082551
Tagagriculture
Taskinstance segmentation
Release YearMade in 2021
LicenseCC BY-SA 4.0
Download1 GB

Introduction #

Luis Enrique Montoya Cavero, Jesús Escobedo

The Sweet Pepper and Peduncle Segmentation dataset was created as part of a master’s research project. It consisted of using an RGB-D-based sensor to detect peppers and their peduncles and then using depth information to estimate their peduncle’s pose. The data gathered here was obtained by moving the sensor horizontally facing sweet pepper crops in a greenhouse. The sensor had a distance of 0.6 meters from the crops. It used default settings and a resolution of 1280x720 pixels.

Characteristic Value
Total Images 620
Classes 2: fruit, peduncle
Attributes per class 4: green, red, yellow, orange
Avg. crop distance 60 cm
Total labeling time 64 hours
ExpandExpand
Dataset LinkHomepageDataset LinkGitHub

Summary #

Sweet Pepper and Peduncle Segmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 620 images with 6422 labeled objects belonging to 8 different classes including green fruit, red fruit, green peduncle, and other: yellow fruit, red peduncle, yellow peduncle, orange fruit, and orange peduncle.

Images in the Sweet Pepper 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. There is 1 unlabeled image (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021.

Here is the visualized example grid with animated annotations:

Explore #

Sweet Pepper dataset has 620 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 Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
OpenSample annotation mask from Sweet PepperSample image from Sweet Pepper
👀
Have a look at 620 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
green fruit
polygon
591
2434
4.12
1.88%
red fruit
polygon
445
2218
4.98
1.8%
green peduncle
polygon
373
620
1.66
0.2%
yellow fruit
polygon
264
569
2.16
0.99%
red peduncle
polygon
222
365
1.64
0.22%
yellow peduncle
polygon
95
110
1.16
0.14%
orange fruit
polygon
86
104
1.21
0.1%
orange peduncle
polygon
2
2
1
0.03%

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
green fruit
polygon
2434
0.44%
12.74%
0.01%
9px
1.25%
405px
56.25%
71px
9.83%
11px
0.86%
421px
32.89%
red fruit
polygon
2218
0.35%
5.11%
0.01%
9px
1.25%
242px
33.61%
63px
8.82%
7px
0.55%
265px
20.7%
green peduncle
polygon
620
0.12%
2.09%
0.01%
7px
0.97%
154px
21.39%
41px
5.64%
10px
0.78%
182px
14.22%
yellow fruit
polygon
569
0.45%
2.33%
0.01%
8px
1.11%
166px
23.06%
66px
9.17%
9px
0.7%
183px
14.3%
red peduncle
polygon
365
0.13%
1.62%
0.01%
10px
1.39%
141px
19.58%
42px
5.79%
11px
0.86%
173px
13.52%
yellow peduncle
polygon
110
0.12%
0.39%
0.01%
14px
1.94%
79px
10.97%
43px
6.03%
14px
1.09%
106px
8.28%
orange fruit
polygon
104
0.08%
2.23%
0.01%
9px
1.25%
175px
24.31%
28px
3.89%
9px
0.7%
149px
11.64%
orange peduncle
polygon
2
0.02%
0.04%
0.01%
13px
1.81%
28px
3.89%
20px
2.85%
12px
0.94%
37px
2.89%

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 6422 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 6422
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
126px
17.5%
142px
11.09%
1.56%
2
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
41px
5.69%
38px
2.97%
0.08%
3
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
90px
12.5%
121px
9.45%
0.53%
4
green fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
51px
7.08%
104px
8.12%
0.23%
5
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
57px
7.92%
61px
4.77%
0.26%
6
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
54px
7.5%
65px
5.08%
0.13%
7
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
42px
5.83%
42px
3.28%
0.05%
8
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
21px
2.92%
38px
2.97%
0.05%
9
red fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
14px
1.94%
28px
2.19%
0.02%
10
green fruit
polygon
_Color_1607626338633.53295898437500.png
720 x 1280
33px
4.58%
40px
3.12%
0.09%

License #

Sweet Pepper and Peduncle Segmentation is under CC BY-SA 4.0 license.

Source

Citation #

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

@dataset{Sweet Pepper,
	authors={Luis Enrique Montoya Caveroand Jesús Escobedo},
	title={Sweet Pepper and Peduncle Segmentation},
	year={2021},
	url={https://www.kaggle.com/datasets/lemontyc/sweet-pepper?resource=download}
}

Source

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

@misc{ visualization-tools-for-sweet-pepper-dataset,
  title = { Visualization Tools for Sweet Pepper Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/sweet-pepper } },
  url = { https://datasetninja.com/sweet-pepper },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-03 },
}

Download #

Dataset Sweet Pepper 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='Sweet Pepper', 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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