detect_chess_pieces / README.md
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---
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-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:[email protected])
### 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](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
[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]