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:

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

Source

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

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.