LaboroTomato: Instance Segmentation Dataset is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the agricultural industry.

The dataset consists of 804 images with 10610 labeled objects belonging to 6 different classes including b_green, l_green, l_fully_ripened, and other: b_half_ripened, l_half_ripened, and b_fully_ripened.

Images in the LaboroTomato 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. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: Train (643 images) and Test (161 images). The dataset was released in 2020 by the Laboro.AI Inc, Japan.

Here is the visualized example grid with animated annotations:

LaboroTomato: Instance Segmentation Dataset is under CC BY-NC-SA 4.0 license.

Source

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

@dataset{LaboroTomato,
	author={Roman Trigubenko and hfujihara},
	title={LaboroTomato: Instance Segmentation Dataset},
	year={2020},
	url={https://github.com/laboroai/LaboroTomato#readme}
}

Source

Dataset LaboroTomato 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='LaboroTomato', 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.