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Supervisely HRDA Plants Demo Dataset

228421
Tagtutorial, featured
Taskinstance segmentation
Release YearMade in 2023
LicenseCC BY-SA 4.0
Download3 GB

Introduction #

The Supervisely HRDA Plants Demo dataset is a part of Sugar Beets 2016 dataset, which is used in the experiment “How to Train a Model with Only 62 Labeled Images using Semi-supervised Learning” conducted by Supervisely Team. The experiment reveals the potential of semi-supervised learning and proves the opportunity to train a good model using only a small fraction of labeled data, thereby significantly minimizing manual effort and the cost of data labeling.

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Dataset LinkBlog Post

Summary #

Supervisely HRDA Plants Demo is a dataset for instance segmentation, semantic segmentation, and semi supervised learning tasks. Possible applications of the dataset could be in the agricultural and robotics industries. The dataset presented here is not the original one. Learn more on the dataset’s homepage.

The dataset consists of 2284 images with 1482 labeled objects belonging to 2 different classes including sugar beet and weed.

Images in the Supervisely HRDA Plants Demo 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 2000 (88% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: unlabeled (2000 images), validation (222 images), and labeled (62 images). The dataset was released in 2023 by the Supervisely.

Here is the visualized example grid with animated annotations:

Explore #

Supervisely HRDA Plants Demo dataset has 2284 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 Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
OpenSample annotation mask from Supervisely HRDA Plants DemoSample image from Supervisely HRDA Plants Demo
👀
Have a look at 2284 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
Images
Objects
Count on image
average
Area on image
average
sugar beet
mask
281
622
2.21
1.47%
weed
mask
188
860
4.57
0.65%

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 1-2 of 2
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
weed
mask
860
0.14%
39.11%
0%
4px
0.41%
957px
99.07%
39px
4.02%
4px
0.31%
853px
65.82%
sugar beet
mask
622
0.66%
10.08%
0.01%
5px
0.52%
632px
65.42%
101px
10.49%
9px
0.69%
632px
48.77%

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 1482 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 1482
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
sugar beet
mask
47_00835_00835_rgb_bonirob_2016-05-10-11-34-24_14_frame262.png
966 x 1296
26px
2.69%
16px
1.23%
0.03%
2
sugar beet
mask
47_00835_00835_rgb_bonirob_2016-05-10-11-34-24_14_frame262.png
966 x 1296
17px
1.76%
48px
3.7%
0.05%
3
sugar beet
mask
47_00835_00835_rgb_bonirob_2016-05-10-11-34-24_14_frame262.png
966 x 1296
147px
15.22%
127px
9.8%
0.39%
4
weed
mask
47_00835_00835_rgb_bonirob_2016-05-10-11-34-24_14_frame262.png
966 x 1296
21px
2.17%
25px
1.93%
0.02%
5
weed
mask
47_00835_00835_rgb_bonirob_2016-05-10-11-34-24_14_frame262.png
966 x 1296
19px
1.97%
22px
1.7%
0.02%
6
sugar beet
mask
04_10925_12522_rgb_bonirob_2016-04-27-16-27-39_27_frame267.png
966 x 1296
13px
1.35%
42px
3.24%
0.03%
7
weed
mask
04_10925_12522_rgb_bonirob_2016-04-27-16-27-39_27_frame267.png
966 x 1296
30px
3.11%
14px
1.08%
0.02%
8
sugar beet
mask
38_09779_11347_rgb_bonirob_2016-05-17-11-42-26_14_frame76.png
966 x 1296
233px
24.12%
341px
26.31%
1.86%
9
sugar beet
mask
38_09779_11347_rgb_bonirob_2016-05-17-11-42-26_14_frame76.png
966 x 1296
249px
25.78%
221px
17.05%
1.73%
10
weed
mask
38_09779_11347_rgb_bonirob_2016-05-17-11-42-26_14_frame76.png
966 x 1296
22px
2.28%
26px
2.01%
0.03%

License #

Supervisely HRDA Plants Demo is under CC BY-SA 4.0 license.

Source

Citation #

If you make use of the Supervisely HRDA Plants Demo data, please cite the following reference:

@dataset{Supervisely HRDA Plants Demo,
  author={Supervisely},
  title={Supervisely HRDA Plants Demo},
  year={2023},
  url={https://www.ipb.uni-bonn.de/data/sugarbeets2016/}
}

Source

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

@misc{ visualization-tools-for-supervisely-hrda-plants-demo-dataset,
  title = { Visualization Tools for Supervisely HRDA Plants Demo Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/supervisely-hrda-plants-demo } },
  url = { https://datasetninja.com/supervisely-hrda-plants-demo },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset Supervisely HRDA Plants Demo 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='Supervisely HRDA Plants Demo', 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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