Bee Image Object Detection

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.

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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.

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.

Sample annotation mask from Bee Image Object Detection

Sample image from Bee Image Object Detection

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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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	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.

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.

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.

Rows 1-10 of 21240

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➔	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 = { nov },
  note = { visited on 2024-11-21 },
}

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.

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