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CWFID Dataset

6022219
Tagagriculture, robotics
Taskinstance segmentation
Release YearMade in 2015
Licensecustom
Download85 MB

Introduction #

Released 2015-04-20 ·Sebastian Haug, Jörn Ostermann

The authors introduce CWFID: A Crop/Weed Field Image Dataset as a standardized dataset for addressing tasks such as distinguishing between crops and weeds, conducting single plant phenotyping, and other computer vision challenges in precision agriculture. These images were captured in an organic carrot farm during the early true leaf growth stage of the carrot plants, utilizing the autonomous field robot Bonirob. The dataset encompasses scenarios involving both intra- and inter-row weeds, where weeds and crops share similar size and grow in close proximity.

Each image in the dataset is accompanied by a ground truth vegetation segmentation mask, and the authors have manually annotated the plant type (crop or weed) for further reference. They also present preliminary findings for solving the crop/weed classification phenotyping problem and introduce evaluation methodologies to facilitate comparisons between various approaches.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

CWFID: A Crop/Weed Field Image Dataset 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 60 images with 492 labeled objects belonging to 2 different classes including crop and weed.

Images in the CWFID 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 no pre-defined train/val/test splits in the dataset. The dataset was released in 2015 by the Bosch Research and Leibniz Universitat Hannover.

Here is the visualized example grid with animated annotations:

Explore #

CWFID dataset has 60 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 CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
OpenSample annotation mask from CWFIDSample image from CWFID
👀
Have a look at 60 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
Images
Objects
Count on image
average
Area on image
average
crop
mask
60
162
2.7
1.61%
weed
mask
59
330
5.59
5.85%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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 1-2 of 2
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
weed
mask
330
1.05%
7.19%
0.03%
29px
3%
682px
70.6%
186px
19.28%
36px
2.78%
600px
46.3%
crop
mask
162
0.6%
1.98%
0.08%
42px
4.35%
272px
28.16%
137px
14.2%
39px
3.01%
308px
23.77%

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 492 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 492
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
weed
mask
003_image.png
966 x 1296
176px
18.22%
163px
12.58%
1.08%
2
weed
mask
003_image.png
966 x 1296
238px
24.64%
282px
21.76%
1.73%
3
weed
mask
003_image.png
966 x 1296
130px
13.46%
323px
24.92%
1.14%
4
crop
mask
003_image.png
966 x 1296
84px
8.7%
206px
15.9%
0.3%
5
crop
mask
003_image.png
966 x 1296
248px
25.67%
236px
18.21%
1.7%
6
weed
mask
003_image.png
966 x 1296
165px
17.08%
104px
8.02%
0.37%
7
weed
mask
003_image.png
966 x 1296
107px
11.08%
114px
8.8%
0.39%
8
weed
mask
003_image.png
966 x 1296
167px
17.29%
166px
12.81%
1.13%
9
weed
mask
011_image.png
966 x 1296
160px
16.56%
97px
7.48%
0.41%
10
weed
mask
011_image.png
966 x 1296
319px
33.02%
224px
17.28%
1.26%

License #

All data is subject to copyright and may only be used for non-commercial research. In case of use please cite our publication.

Contact Sebastian Haug sebastian.haug@de.bosch.com for any questions.

Source

Citation #

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

@inproceedings{haug15,
  author={Haug, Sebastian and Ostermann, J{\"o}rn},
  title={A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks},
  year={2015},
  booktitle={Computer Vision - ECCV 2014 Workshops},
  doi={10.1007/978-3-319-16220-1_8},
  url={http://dx.doi.org/10.1007/978-3-319-16220-1_8},
  pages={105--116},
}

Source

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

@misc{ visualization-tools-for-cwfid-dataset,
  title = { Visualization Tools for CWFID Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/cwfid } },
  url = { https://datasetninja.com/cwfid },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset CWFID 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='CWFID', 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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