
# Dataset Card for Object Detection for Chess Pieces

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** https://github.com/faizankshaikh/chessDetection
- **Repository:** https://github.com/faizankshaikh/chessDetection
- **Paper:** -
- **Leaderboard:** -
- **Point of Contact:** [Faizan Shaikh](mailto:faizankshaikh@gmail.com)

### 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`: A `PIL.Image.Image` object 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](https://github.com/faizankshaikh/chessDetection/blob/main/code/1.1%20create_images_with_labels.ipynb) 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]
task_categories:object-detection annotations_creators:machine-generated language_creators:machine-generated multilinguality:monolingual size_categories:n<1K language:en license:other region:us
{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": [], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "Object Detection for Chess Pieces"}
TAGS
#task_categories-object-detection #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us 

Dataset Card for Object Detection for Chess Pieces
==================================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Paper: -
* Leaderboard: -
* Point of Contact: Faizan Shaikh


### 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.


### Data Fields


* 'image': A 'PIL.Image.Image' object containing the 224x224 image.
* 'label': An integer between 0 and 3 representing the classes with the following mapping:
* '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?


### 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


### 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



### 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. ### Data Fields


* 'image': A 'PIL.Image.Image' object containing the 224x224 image.
* 'label': An integer between 0 and 3 representing the classes with the following mapping:
* '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? ### 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 ### 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
TAGS
#task_categories-object-detection #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us 
 ### 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. ### Data Fields


* 'image': A 'PIL.Image.Image' object containing the 224x224 image.
* 'label': An integer between 0 and 3 representing the classes with the following mapping:
* '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? ### 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 ### 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
passage: TAGS
#task_categories-object-detection #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us 
### 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.### Data Fields


* 'image': A 'PIL.Image.Image' object containing the 224x224 image.
* 'label': An integer between 0 and 3 representing the classes with the following mapping:
* '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?### 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
---------------------------------