Synthetic GWHD is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 15448 images with 621136 labeled objects belonging to 1 single class (wheat).

Images in the Synthetic GWHD dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 4 splits in the dataset: style_transfer_images (10108 images), corrected_train (3373 images), pix2pix_1_synthetic (1267 images), and pix2pix_2_synthetic (700 images). Additionally, every image has source tag. The dataset was released in 2022.

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Source

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

@dataset{Synthetic GWHD,
  author={Bendangnuksung},
  title={Synthetic GWHD},
  year={2022},
  url={https://www.kaggle.com/datasets/bendang/synthetic-wheat-images}
}

Source

Dataset Synthetic GWHD 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 GWHD', 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.