Supervisely Potatoes Dataset for Crops and Weed Segmentation is a dataset for a weakly supervised learning task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 1055 images with 3971 labeled objects belonging to 2 different classes including crop and weed.

Images in the Supervisely Potatoes 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 82 (8% of the total) unlabeled images (i.e. without annotations). There is 1 split in the dataset: train (1055 images). Alternatively, the dataset could be split into 3 field IDs: 113 (935 images), 114 (60 images), and 116 (60 images). The dataset was released in 2023 by the Supervisely.

Here is the visualized example grid with animated annotations:

Supervisely Potatoes Dataset for Crops and Weed Segmentation is under CC BY-NC-SA 4.0 license.

Source

If you make use of the Supervisely Potatoes data, please cite the following reference:

@dataset{Supervisely Potatoes,
  author={Supervisely},
  title={Supervisely Potatoes Dataset for Crops and Weed Segmentation},
  year={2023},
  url={https://supervisely.com/blog/train-a-model-with-62-labeled-images-hrda-semi-supervised/}
}

Source

Dataset Supervisely Potatoes 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 Potatoes', 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.