Carrot-Weed - Dataset Ninja

Introduction #

Released 2017-07-23 ·Petre Lameski, Eftim Zdravevski, Vladimir Trajkoviket al.

Weed detection from images has garnered substantial attention from scientific communities in recent years. However, there are only a few available datasets suitable for weed detection from unmanned and other ground vehicles and systems. Addressing mentioned issues, the authors introduce a new dataset named Carrot-Weed designed for weed detection under varying light conditions. This dataset comprises RGB images captured from young carrot seedlings throughout the month of February in the vicinity of Negotino, Republic of Macedonia. The authors conducted an initial analysis of the dataset and reported their preliminary findings, achieved through the utilization of convolutional neural network architectures.

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Research Paper

Summary #

Carrot-Weed: Weed Detection Dataset with RGB Images Taken under Variable Light Conditions is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the agricultural research.

The dataset consists of 39 images with 745 labeled objects belonging to 2 different classes including weed and carrot.

Images in the Carrot-Weed 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 2017 by the University of Sts. Cyril and Methodius in Skopje, Macedonia.

Here is the visualized example grid with animated annotations:

Explore #

Carrot-Weed dataset has 39 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 39 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.

Rows 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
weed➔ mask	39	624	16	5.45%
carrot➔ mask	39	121	3.1	8.53%

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.

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	624	0.34%	8.56%	0.04%	33px	1.35%	2448px	100%	262px	10.7%	40px	1.23%	3264px	100%
carrot mask	121	2.75%	9.59%	0.05%	86px	3.51%	1739px	71.04%	703px	28.7%	85px	2.6%	1724px	52.82%

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 745 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 745

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➔	weed mask	IMG_3450.JPG	2448 x 3264	95px	3.88%	198px	6.07%	0.1%
2➔	weed mask	IMG_3450.JPG	2448 x 3264	114px	4.66%	46px	1.41%	0.04%
3➔	weed mask	IMG_3450.JPG	2448 x 3264	1018px	41.58%	1108px	33.95%	2.88%
4➔	weed mask	IMG_3450.JPG	2448 x 3264	125px	5.11%	68px	2.08%	0.04%
5➔	weed mask	IMG_3450.JPG	2448 x 3264	108px	4.41%	146px	4.47%	0.04%
6➔	weed mask	IMG_3450.JPG	2448 x 3264	1019px	41.63%	1038px	31.8%	3.67%
7➔	weed mask	IMG_3450.JPG	2448 x 3264	136px	5.56%	422px	12.93%	0.22%
8➔	weed mask	IMG_3450.JPG	2448 x 3264	158px	6.45%	121px	3.71%	0.07%
9➔	weed mask	IMG_3450.JPG	2448 x 3264	214px	8.74%	290px	8.88%	0.15%
10➔	weed mask	IMG_3450.JPG	2448 x 3264	500px	20.42%	782px	23.96%	1.56%

License #

The dataset can be used freely for non-commercial applications. For any additional information please contact: Petre Lameski petre.lameski@finki.ukim.mk

Source

Citation #

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

Petre Lameski, Eftim Zdravevski, Vladimir Trajkovik, Andrea Kulakov, "Weed detection dataset with RGB images taken under variable light conditions", 9th International Conference ICT Innovations 2017, 18-21 September, Skopje, Macedonia

Source

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

@misc{ visualization-tools-for-weed-dataset,
  title = { Visualization Tools for Carrot-Weed Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/weed } },
  url = { https://datasetninja.com/weed },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Carrot-Weed 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='Carrot-Weed', 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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Disclaimer #

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