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Synthetic Plants Dataset

1000042267
Tagagriculture
Taskinstance segmentation
Release YearMade in 2018
LicenseCC0 1.0
Download1 GB

Summary #

Dataset LinkHomepage

Synthetic RGB-D Data for Plant 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 10000 images with 326754 labeled objects belonging to 4 different classes including leaf, petiole, stem, and other: fruit.

Images in the Synthetic Plants 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 are 2 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2018.

Here is the visualized example grid with animated annotations:

Explore #

Synthetic Plants dataset has 10000 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 Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
OpenSample annotation mask from Synthetic PlantsSample image from Synthetic Plants
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Have a look at 10000 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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 1-4 of 4
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
leafβž”
mask
9993
194890
19.5
12.97%
petioleβž”
mask
9992
87578
8.76
4.62%
stemβž”
mask
9981
19673
1.97
2.81%
fruitβž”
mask
8715
24613
2.82
3.5%

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 1-4 of 4
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
leaf
mask
194890
0.67%
40.02%
0.1%
3px
1.34%
224px
100%
22px
10%
3px
1.34%
224px
100%
petiole
mask
87578
0.53%
5.91%
0.1%
2px
0.89%
224px
100%
41px
18.45%
2px
0.89%
224px
100%
fruit
mask
24613
1.24%
23.98%
0.1%
3px
1.34%
157px
70.09%
28px
12.64%
3px
1.34%
163px
72.77%
stem
mask
19673
1.42%
7.24%
0.1%
5px
2.23%
224px
100%
101px
44.92%
2px
0.89%
94px
41.96%

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 326754 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 326754
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”
stem
mask
Image2867.png
224 x 224
29px
12.95%
5px
2.23%
0.12%
2βž”
stem
mask
Image2867.png
224 x 224
18px
8.04%
6px
2.68%
0.13%
3βž”
stem
mask
Image2867.png
224 x 224
38px
16.96%
9px
4.02%
0.4%
4βž”
stem
mask
Image2867.png
224 x 224
58px
25.89%
13px
5.8%
0.82%
5βž”
leaf
mask
Image2867.png
224 x 224
7px
3.12%
22px
9.82%
0.17%
6βž”
leaf
mask
Image2867.png
224 x 224
31px
13.84%
42px
18.75%
1.41%
7βž”
leaf
mask
Image2867.png
224 x 224
14px
6.25%
19px
8.48%
0.26%
8βž”
leaf
mask
Image2867.png
224 x 224
60px
26.79%
64px
28.57%
2.56%
9βž”
leaf
mask
Image2867.png
224 x 224
11px
4.91%
11px
4.91%
0.14%
10βž”
leaf
mask
Image2867.png
224 x 224
30px
13.39%
53px
23.66%
1.41%

License #

Synthetic RGB-D Data for Plant Segmentation is under CC0 1.0 license.

Source

Citation #

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

@dataset{Synthetic Plants,
	author={El Baha Farouk},
	title={Synthetic RGB-D Data for Plant Segmentation},
	year={2018},
	url={https://www.kaggle.com/datasets/harlequeen/synthetic-rgbd-images-of-plants}
}

Source

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

@misc{ visualization-tools-for-synthetic-plants-dataset,
  title = { Visualization Tools for Synthetic Plants Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/synthetic-plants } },
  url = { https://datasetninja.com/synthetic-plants },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Synthetic Plants 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='Synthetic Plants', 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.

. . .

Disclaimer #

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