Dataset Ninja LogoDataset Ninja:

Bee Image Object Detection Dataset

304411
Taglivestock, agriculture
Taskobject detection
Release YearMade in 2022
LicenseCC BY-NC-ND 4.0
Download6 GB

Introduction #

AndrewLCA

The Bee Image Object Detection dataset was generated for the purpose of detecting bee objects within images. The dataset comprises videos captured at the entrances of 25 beehives situated in three separate apiaries in San Jose, Cupertino, and Gilroy, CA, USA. These videos were recorded directly above the landing pads of various beehives. The camera was positioned at a unique angle to capture distinct and clear images of bees engaged in activities such as taking off, landing, or moving around on the landing pad.

The images were saved one frame per second from videos. The annotation platform Label Studio was selected to annotate bees in each image due to the friendly user interface and high quality. The below criteria were followed in the labeling process. First, at least 50% of the bee’s body must be visible. Second, the image cannot be too blurry. After tagging each bee with a rectangle box in the annotation tool, output label files with Yolo labeling format were generated for each image. The output label files contained bounding box coordinates for each of the bees in the image. If there were multiple objects in the image, there would be one line for one object in the label file. It recorded the object ID, X-axis center, Y-axis center, BBox width and height with normalized image size from 0 to 1.

ExpandExpand
Dataset LinkHomepage

Summary #

Bee Image Object Detection is a dataset for an object detection task. Possible applications of the dataset could be in the livestock and agricultural industries.

The dataset consists of 3044 images with 21240 labeled objects belonging to 1 single class (bee).

Images in the Bee Image Object Detection dataset have bounding box annotations. There are 2 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2022.

Dataset Poster

Explore #

Bee Image Object Detection dataset has 3044 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 Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
OpenSample annotation mask from Bee Image Object DetectionSample image from Bee Image Object Detection
👀
Have a look at 3044 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
bee
rectangle
3042
21240
6.98
7.64%

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.

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 1-1 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
bee
rectangle
21240
1.22%
9.49%
0%
2px
0.28%
1284px
42.46%
183px
11.98%
1px
0.08%
1133px
43.19%

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 21240 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 21240
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
bee
rectangle
IMG_4614.jpeg
3024 x 4032
592px
19.58%
737px
18.28%
3.58%
2
bee
rectangle
camp_IMG_7263_435.jpeg
720 x 1280
67px
9.31%
74px
5.78%
0.54%
3
bee
rectangle
camp_IMG_7263_435.jpeg
720 x 1280
99px
13.75%
85px
6.64%
0.91%
4
bee
rectangle
IMG_9700.jpeg
3024 x 4032
419px
13.86%
420px
10.42%
1.44%
5
bee
rectangle
IMG_9700.jpeg
3024 x 4032
425px
14.05%
430px
10.66%
1.5%
6
bee
rectangle
camp_IMG_7273_1363.jpeg
1280 x 720
161px
12.58%
137px
19.03%
2.39%
7
bee
rectangle
camp_IMG_7273_1363.jpeg
1280 x 720
171px
13.36%
135px
18.75%
2.5%
8
bee
rectangle
camp_IMG_7273_1363.jpeg
1280 x 720
174px
13.59%
159px
22.08%
3%
9
bee
rectangle
camp_IMG_7273_1363.jpeg
1280 x 720
155px
12.11%
161px
22.36%
2.71%
10
bee
rectangle
camp_IMG_7273_1363.jpeg
1280 x 720
157px
12.27%
167px
23.19%
2.84%

License #

Bee Image Object Detection is under CC BY-NC-ND 4.0 license.

Source

Citation #

If you make use of the Bee Image Object Detection data, please cite the following reference:

 @misc{andrewl_2022,
	title={Bee Image Object Detection},
	url={https://www.kaggle.com/dsv/4738309},
	DOI={10.34740/KAGGLE/DSV/4738309},
	publisher={Kaggle},
	author={AndrewL},
	year={2022}
}

Source

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

@misc{ visualization-tools-for-bee-image-dataset,
  title = { Visualization Tools for Bee Image Object Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/bee-image } },
  url = { https://datasetninja.com/bee-image },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Bee Image Object Detection 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='Bee Image Object Detection', 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 #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.