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.
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.
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.
Class ㅤ | Images ㅤ | Objects ㅤ | 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.
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.
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.
Object ID ㅤ | Class ㅤ | Image name click row to open | Image size height x width | Height ㅤ | Height ㅤ | Width ㅤ | Width ㅤ | Area ㅤ |
---|---|---|---|---|---|---|---|---|
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.
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/}
}
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 = { oct },
note = { visited on 2024-10-15 },
}
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.
Disclaimer #
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