Sweet Pepper and Peduncle Segmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 620 images with 6422 labeled objects belonging to 8 different classes including green fruit, red fruit, green peduncle, and other: yellow fruit, red peduncle, yellow peduncle, orange fruit, and orange peduncle.

Images in the Sweet Pepper 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 is 1 unlabeled image (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021.

Here is the visualized example grid with animated annotations:

Sweet Pepper and Peduncle Segmentation is under CC BY-SA 4.0 license.

Source

If you make use of the Sweet Pepper data, please cite the following reference:

@dataset{Sweet Pepper,
	authors={Luis Enrique Montoya Caveroand Jesús Escobedo},
	title={Sweet Pepper and Peduncle Segmentation},
	year={2021},
	url={https://www.kaggle.com/datasets/lemontyc/sweet-pepper?resource=download}
}

Source

Dataset Sweet Pepper 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='Sweet Pepper', 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.