The MangoNet Semantic Dataset is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the agricultural industry.

The dataset consists of 49 images with 6799 labeled objects belonging to 1 single class (mango).

Images in the MangoNet 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_data (45 images) and test_data (4 images). The dataset was released in 2018 by the Indian Institute of Science and Birla Institute of Technology and Science, India.

Here is the visualized example grid with animated annotations:

The MangoNet Semantic Dataset is under MIT license.

Source

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

@article{KESTUR201959,
  title = {MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {77},
  pages = {59-69},
  year = {2019},
  issn = {0952-1976},
  doi = {https://doi.org/10.1016/j.engappai.2018.09.011},
  url = {https://www.sciencedirect.com/science/article/pii/S0952197618301970},
  author = {Ramesh Kestur and Avadesh Meduri and Omkar Narasipura},
  keywords = {Deep learning, Semantic segmentation, Object detection, Mango detection, Convolutional neural networks, Fully convolutional networks},
  abstract = {This work presents a method for detection and counting of mangoes in RGB images for further yield estimation. The RGB images are acquired in open field conditions from a mango orchard in the pre-harvest stage. The proposed method uses MangoNet, a deep convolutional neural network based architecture for mango detection using semantic segmentation. Further, mango objects are detected in the semantic segmented output using contour based connected object detection. The MangoNet is trained using 11,096 image patches of size 200×200 obtained from 40 images. Testing was carried out on 1500 image patches generated from 4 test images. The results are analyzed for performance of segmentation and detection of mangoes. Results are analyzed using the precision, recall, F1 parameters derived from contingency matrix. Results demonstrate the robustness of detection for a multitude of factors such as scale, occlusion, distance and illumination conditions, characteristic to open field conditions. The performance of the MangoNet is compared with FCN variant architectures trained on the same data. MangoNet outperforms its variant architectures.}
}

Source

Dataset MangoNet 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='MangoNet', 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.