MinneApple - Dataset Ninja

Introduction #

Released 2019-09-13 ·Nicolai Häni, Pravakar Roy, Volkan Isler

The authors of the MinneApple: A Benchmark Dataset for Apple Detection and Segmentation introduce claim the need for advancing the current state-of-the-art in fruit detection, segmentation, and counting, particularly within orchard environments. This aim is pursued by offering an extensive collection of high-resolution images captured in orchards, complemented by human-generated annotations for the fruits present on the trees. The dataset serves a dual purpose, addressing two distinct subproblems integral to the broader challenge of yield estimation. Accurate fruit detection is essential for yield estimation, with the added complexity of clustered fruits necessitating a separate counting algorithm in most instances. Notably, apple objects are delineated with polygon masks, facilitating precise object detection, localization, and segmentation. Furthermore, the dataset includes data suitable for patch-based counting of clustered fruits, encompassing over 40,000 annotated object instances distributed across 1,000 images.

The data collection process primarily involves the capture of video footage from various sections of the orchard. A standard Samsung Galaxy S4 cell phone was employed for this purpose. During data acquisition, the camera was oriented horizontally, focusing on a single side of a tree row. Subsequently, individual images were extracted from these video sequences.

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Research Paper

GitHub

Summary #

MinneApple: A Benchmark Dataset for Apple Detection and Segmentation is a dataset for instance segmentation, semantic segmentation, object detection, and counting tasks. It is used in the agricultural industry.

The dataset consists of 69982 images with 23065 labeled objects belonging to 1 single class (apple).

Images in the MinneApple 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 are 69312 (99% of the total) unlabeled images (i.e. without annotations). There are 5 splits in the dataset: counting-train (64595 images), counting-val (3395 images), counting-test (991 images), detection-train (670 images), and detection-test (331 images). The dataset was released in 2019 by the University of Minnesota Robotic Sensor Network Laboratory.

Here is the visualized example grid with animated annotations:

Explore #

MinneApple dataset has 69982 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

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Have a look at 69982 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
apple➔ mask	670	23065	34.43	2.64%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-1 of 1

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
apple mask	23065	0.08%	1.08%	0%	2px	0.16%	148px	11.56%	31px	2.44%	2px	0.28%	188px	26.11%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 23065 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 23065

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	apple mask	20150921_131453_image1101.png	1280 x 720	31px	2.42%	39px	5.42%	0.08%
2➔	apple mask	20150921_131453_image1101.png	1280 x 720	18px	1.41%	19px	2.64%	0.02%
3➔	apple mask	20150921_131453_image1101.png	1280 x 720	34px	2.66%	22px	3.06%	0.06%
4➔	apple mask	20150921_131453_image1101.png	1280 x 720	37px	2.89%	39px	5.42%	0.1%
5➔	apple mask	20150921_131453_image1101.png	1280 x 720	35px	2.73%	36px	5%	0.1%
6➔	apple mask	20150921_131453_image1101.png	1280 x 720	31px	2.42%	26px	3.61%	0.05%
7➔	apple mask	20150921_131453_image1101.png	1280 x 720	39px	3.05%	34px	4.72%	0.11%
8➔	apple mask	20150921_131453_image1101.png	1280 x 720	40px	3.12%	40px	5.56%	0.13%
9➔	apple mask	20150921_131453_image1101.png	1280 x 720	36px	2.81%	43px	5.97%	0.11%
10➔	apple mask	20150921_131453_image1101.png	1280 x 720	32px	2.5%	65px	9.03%	0.11%

License #

MinneApple: A Benchmark Dataset for Apple Detection and Segmentation is under CC BY-NC-SA 3.0 US license.

Source

Citation #

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

Haeni, Nicolai; Roy, Pravakar; Isler, Volkan. (2019). MinneApple: A Benchmark Dataset for Apple Detection and Segmentation. Retrieved from the Data Repository for the University of Minnesota, https://doi.org/10.13020/8ecp-3r13.

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-minne-apple-dataset,
  title = { Visualization Tools for MinneApple Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/minne-apple } },
  url = { https://datasetninja.com/minne-apple },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { dec },
  note = { visited on 2024-12-02 },
}

Download #

Dataset MinneApple 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='MinneApple', 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:

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