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Synthetic GWHD Dataset

1544811314
Tagagriculture
Taskobject detection
Release YearMade in 2022
Licensecustom
Download9 GB

Introduction #

Bendangnuksung

The Synthetic GWHD dataset was made as part of participation for the Global Wheat Detection Competition. The images were generated through the application of Style Transfer and Pix2Pix techniques. The dataset includes the original images in corrected_train split. This dataset could be considered as an augmentation of original GWHD dataset (available on DatasetNinja)

image

Style Transfer Images: This was created using 25 different styles.

image

Pix2Pix: Single Generation.

image

Pix2Pix: Mosiac Generation.

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Dataset LinkHomepage

Summary #

Synthetic GWHD is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 15448 images with 621136 labeled objects belonging to 1 single class (wheat).

Images in the Synthetic GWHD dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 4 splits in the dataset: style_transfer_images (10108 images), corrected_train (3373 images), pix2pix_1_synthetic (1267 images), and pix2pix_2_synthetic (700 images). Additionally, every image has source tag. The dataset was released in 2022.

Dataset Poster

Explore #

Synthetic GWHD dataset has 15448 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.

OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
OpenSample annotation mask from Synthetic GWHDSample image from Synthetic GWHD
👀
Have a look at 15448 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.

Search
Rows 1-1 of 1
Class
Images
Objects
Count on image
average
Area on image
average
wheat
rectangle
15448
621136
40.21
26.73%

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.

Search
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
wheat
rectangle
621136
0.72%
32.86%
0%
1px
0.1%
652px
63.67%
81px
7.91%
1px
0.1%
699px
68.26%

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.

Spatial Heatmap

Objects #

Table contains all 102093 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.

Search
Rows 1-10 of 102093
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
108px
10.55%
119px
11.62%
1.23%
2
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
144px
14.06%
131px
12.79%
1.8%
3
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
98px
9.57%
134px
13.09%
1.25%
4
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
106px
10.35%
178px
17.38%
1.8%
5
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
90px
8.79%
126px
12.3%
1.08%
6
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
96px
9.38%
116px
11.33%
1.06%
7
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
155px
15.14%
185px
18.07%
2.73%
8
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
142px
13.87%
168px
16.41%
2.28%
9
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
93px
9.08%
49px
4.79%
0.43%
10
wheat
rectangle
bd20bc5c9.jpg
1024 x 1024
111px
10.84%
117px
11.43%
1.24%

License #

  1. COMPETITION DATA.
    “Competition Data” means the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to participants.

A. Data Access and Use.
Competition Use and Non-Commercial & Academic Research: You may access and use the Competition Data for non-commercial purposes only, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.

The Competition Data is also subject to the following terms and conditions: https://opensource.org/licenses/MIT. To the extent that the terms and conditions located at the URL conflict with or are inconsistent with these Rules, these Rules will govern your use of the Competition Data.

B. Data Security. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.

C. External Data. You may use data other than the Competition Data (“External Data”) to develop and test your models and Submissions. However, you will (i) ensure the External Data is available to use by all participants of the competition for purposes of the competition at no cost to the other participants and (ii) post such access to the External Data for the participants to the official competition forum prior to the Entry Deadline.

Source

Citation #

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

@dataset{Synthetic GWHD,
  author={Bendangnuksung},
  title={Synthetic GWHD},
  year={2022},
  url={https://www.kaggle.com/datasets/bendang/synthetic-wheat-images}
}

Source

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

@misc{ visualization-tools-for-synthetic-gwhd-dataset,
  title = { Visualization Tools for Synthetic GWHD Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/synthetic-gwhd } },
  url = { https://datasetninja.com/synthetic-gwhd },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Synthetic GWHD 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='Synthetic GWHD', 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.

. . .

Disclaimer #

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