Datasets:
Search is not available for this dataset
image
image | objects
sequence |
|---|---|
{
"label": [
0
],
"bbox": [
[
21,
203,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
21,
177,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
21,
151,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
21,
125,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
21,
99,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
21,
73,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
21,
47,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
21,
21,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
203,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
177,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
151,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
125,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
99,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
73,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
47,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
47,
21,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
73,
203,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
73,
125,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
73,
99,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
73,
47,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
73,
21,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
99,
203,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
99,
177,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
99,
151,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
99,
125,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
99,
99,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
99,
73,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
99,
21,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
125,
203,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
125,
177,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
125,
99,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
125,
73,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
125,
47,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
125,
21,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
151,
203,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
151,
177,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
151,
151,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
151,
125,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
151,
99,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
151,
73,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
151,
21,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
177,
203,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
177,
125,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
177,
73,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
177,
47,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
177,
21,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
203,
177,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
203,
125,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
203,
99,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
203,
73,
26,
26
]
]
} |
|
{
"label": [
0
],
"bbox": [
[
203,
47,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
21,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
21,
151,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
21,
125,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
21,
99,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
21,
73,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
21,
47,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
21,
21,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
203,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
151,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
125,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
99,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
73,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
47,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
47,
21,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
73,
203,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
73,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
73,
151,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
73,
73,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
73,
47,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
99,
203,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
99,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
99,
151,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
99,
125,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
99,
99,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
99,
47,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
99,
21,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
125,
203,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
125,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
125,
151,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
125,
125,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
125,
73,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
125,
47,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
125,
21,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
151,
203,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
151,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
151,
125,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
151,
99,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
151,
73,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
177,
203,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
177,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
177,
125,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
177,
99,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
177,
73,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
177,
21,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
203,
203,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
203,
177,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
203,
125,
26,
26
]
]
} |
|
{
"label": [
2
],
"bbox": [
[
203,
73,
26,
26
]
]
} |
End of preview. Expand
in Dataset Viewer.
Dataset Card for Object Detection for Chess Pieces
Dataset Summary
The "Object Detection for Chess Pieces" dataset is a toy dataset created (as suggested by the name!) to introduce object detection in a beginner friendly way. It is structured in a one object-one image manner, with the objects being of four classes, namely, Black King, White King, Black Queen and White Queen
Supported Tasks and Leaderboards
object-detection: The dataset can be used to train and evaluate simplistic object detection models
Languages
The text (labels) in the dataset is in English
Dataset Structure
Data Instances
A data point comprises an image and the corresponding objects in bounding boxes.
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x23557C66160>,
'objects': { "label": [ 0 ], "bbox": [ [ 151, 151, 26, 26 ] ] }
}
Data Fields
image: APIL.Image.Imageobject containing the 224x224 image.label: An integer between 0 and 3 representing the classes with the following mapping:Label Description 0 blackKing 1 blackQueen 2 whiteKing 3 whiteQueen bbox: A list of integers having sequence [x_center, y_center, width, height] for a particular bounding box
Data Splits
The data is split into training and validation set. The training set contains 204 images and the validation set 52 images.
Dataset Creation
Curation Rationale
The dataset was created to be a simple benchmark for object detection
Source Data
Initial Data Collection and Normalization
The data is obtained by machine generating images from "python-chess" library. Please refer this code to understand data generation pipeline
Who are the source language producers?
[Needs More Information]
Annotations
Annotation process
The annotations were done manually.
Who are the annotators?
The annotations were done manually.
Personal and Sensitive Information
None
Considerations for Using the Data
Social Impact of Dataset
The dataset can be considered as a beginner-friendly toy dataset for object detection. It should not be used for benchmarking state of the art object detection models, or be used for a deployed model.
Discussion of Biases
[Needs More Information]
Other Known Limitations
The dataset only contains four classes for simplicity. The complexity can be increased by considering all types of chess pieces, and by making it a multi-object detection problem
Additional Information
Dataset Curators
The dataset was created by Faizan Shaikh
Licensing Information
The dataset is licensed as CC-BY-SA:2.0
Citation Information
[Needs More Information]
- Downloads last month
- 181