Recognizance'22 Round 2 - Dataset Ninja

Introduction #

Released 2022-11-22 ·Utkarsh Pandey

The Recognizance’22 dataset is a collection of microscopic images in which individual cells have been meticulously segmented. Cell segmentation is a crucial initial stage in numerous biomedical investigations, forming a fundamental aspect of image-based cellular research. The shape and structure of a cell can provide essential insights into its physiological condition, and a precisely segmented image is vital for capturing biologically significant morphological details.

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Summary #

Recognizance’22 Round 2 is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the biological research.

The dataset consists of 1200 images with 6097 labeled objects belonging to 1 single class (cell).

Images in the Recognizance’22 Round 2 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. There are 200 (17% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (1000 images) and test (200 images). The dataset was released in 2022.

Explore #

Recognizance'22 Round 2 dataset has 1200 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 Recognizance'22 Round 2

Sample image from Recognizance'22 Round 2

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Have a look at 1200 images

Because of dataset's license preview is limited to 12 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
cell➔ mask	1000	6097	6.1	2.24%

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
cell mask	6097	0.37%	8.44%	0%	18px	0.83%	1103px	51.06%	159px	7.41%	18px	0.83%	1307px	60.51%

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 6097 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 6097

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➔	cell mask	train (10).jpg	2160 x 2160	102px	4.72%	54px	2.5%	0.06%
2➔	cell mask	train (10).jpg	2160 x 2160	66px	3.06%	67px	3.1%	0.05%
3➔	cell mask	train (10).jpg	2160 x 2160	81px	3.75%	104px	4.81%	0.07%
4➔	cell mask	train (10).jpg	2160 x 2160	48px	2.22%	85px	3.94%	0.04%
5➔	cell mask	train (10).jpg	2160 x 2160	83px	3.84%	75px	3.47%	0.06%
6➔	cell mask	train (10).jpg	2160 x 2160	88px	4.07%	43px	1.99%	0.05%
7➔	cell mask	train (10).jpg	2160 x 2160	49px	2.27%	45px	2.08%	0.03%
8➔	cell mask	train (10).jpg	2160 x 2160	57px	2.64%	107px	4.95%	0.06%
9➔	cell mask	train (10).jpg	2160 x 2160	71px	3.29%	73px	3.38%	0.07%
10➔	cell mask	train (10).jpg	2160 x 2160	52px	2.41%	146px	6.76%	0.07%

License #

License is unknown for the Recognizance’22 Round 2 dataset.

Source

Citation #

If you make use of the Recognizance’22 Round 2 data, please cite the following reference:

@dataset{Recognizance'22 Round 2,
  author={Utkarsh Pandey},
  title={Recognizance'22 Round 2},
  year={2022},
  url={https://www.kaggle.com/datasets/kratosishere/recog2}
}

Source

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

@misc{ visualization-tools-for-recognizance22-round-dataset,
  title = { Visualization Tools for Recognizance'22 Round 2 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/recognizance22-round-2 } },
  url = { https://datasetninja.com/recognizance22-round-2 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-01 },
}

Download #

Please visit dataset homepage to download the data.

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Disclaimer #

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