README.md

metadata

annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
languages:
  - en
licenses:
  - cc-by-sa-2.0
multilinguality:
  - monolingual
paperswithcode_id: []
pretty_name: Object Detection for Chess Pieces
size_categories:
  - n<1K
source_datasets: []
task_categories:
  - object-detection
task_ids: []

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: https://github.com/faizankshaikh/chessDetection
• Repository: https://github.com/faizankshaikh/chessDetection
• 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 its label.

{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x23557C66160>,
  'label': 0
}

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

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

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

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]