Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the agricultural industry, and in the biological research.

The dataset consists of 500 images with 2977 labeled objects belonging to 2 different classes including leaf and symptom.

Images in the Coffee Leaf Biotic Stress 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 3 splits in the dataset: Train (400 images), Test (50 images), and Val (50 images). The dataset was released in 2019 by the Federal University of Espírito Santo, Brazil.

Here is the visualized example grid with animated annotations:

Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress is under MIT license.

Source

If you make use of the Coffee Leaf Biotic Stress data, please cite the following reference:

@dataset{Coffee Leaf Biotic Stress,
	author={Guilherme Esgario and Pedro Biasutti},
	title={Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress},
	year={2019},
	url={https://github.com/esgario/lara2018/}
}

Source

Dataset Coffee Leaf Biotic Stress 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='Coffee Leaf Biotic Stress', 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.