README.md

---
annotations_creators:
- machine-generated
language: []
language_creators:
- machine-generated
license:
- mit
multilinguality: []
pretty_name: Blackjack
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- attribute
- concepts
task_categories:
- image-classification
- image-segmentation
task_ids:
- multi-label-image-classification
- instance-segmentation
---

# Dataset Card for Blackjack

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

### Dataset Summary

A dataset containing two sets of playing card images for hands in the card game Blackjack. Each set contains at least 10,000 images and has a series of attributes. This dataset is based on the dataset [Playing cards](https://huggingface.co/datasets/JackFurby/playing-cards) [1]

Train and test splits are provided in both JSON and pickle formats. Concept and task classification labels (both zero indexed) and names are provided in txt files.

## Dataset Structure

### Data Instances

Each set of samples have the following:
* player and dealer playing cards in each sample image
* A list of concepts present in the each sample (1 for concepts present and 0 otherwise)
* The task classification label
* coordinates for each of the corners of playing cards in each sample.

The basic structure of the JSON and pkl files describing each sample is as follows:

```
sample ID, {
	'img_path': string file path,
	'class_label': integer,
    'concept_label': list of 0s and 1s,
	'player_card_points': list of tuples and card class labels as integers
    'dealer_card_points': list of tuples and card class labels as integers
    'game_numer': integer
}
```

#### Standard

Card hands using a single style of playing cards.

* **Concepts**: soft/hard hand, sum of player cards, first dealer card, dealer has multiple cards
* **Class label**: Best move
* **Card points**: Coordinates of the card and card classification

##### Example

```
"14304": {
  "img_path": "imgs/standard/val/0/14304.png",
  "class_label": 0,
  "concept_label": [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
  "player_card_points": [[[[50, 789], [173, 789], [50, 974], [173, 974]], "QS"], [[[185, 789], [308, 789], [185, 974], [308, 974]], "5S"]],
  "dealer_card_points": [[[[172, 235], [50, 235], [172, 50], [50, 50]], "7D"]],
  "game_number": 0
}
```

#### Mixed

Card hands using a one style of playing cards for all Ace and Seven playing cards and a second style for all other cards.

* **Concepts**: soft/hard hand, sum of player cards, first dealer card, dealer has multiple cards
* **Class label**: Best move
* **Card points**: Coordinates of the card and card classification

##### Example

```
"0": {
  "img_path": "imgs/mixed_ace_seven/train/0/0.png",
  "class_label": 0,
  "concept_label": [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
  "player_card_points": [[[[173, 974], [50, 974], [173, 789], [50, 789]], "10S"], [[[185, 789], [308, 789], [185, 974], [308, 974]], "4H"]],
  "dealer_card_points": [[[[172, 235], [50, 235], [172, 50], [50, 50]], "QC"]],
  "game_number": 0
}

```

### Data Fields

* String file path from the root of the dataset to a given samples image file
* A list of concepts present in the each sample (1 for concepts present and 0 otherwise). The index of each value in this list corresponds to the label in concepts.txt.
* The task classification label. This corresponds the the label in classes.txt
* list of playing cards present in a given sample player hand. Each item in the list has a list of card coordinates (card coordinates are always in the order top left, top right, bottom left, bottom right) and the card classification label.
* list of playing cards present in a given sample player hand. Each item in the list has a list of card coordinates (card coordinates are always in the order top left, top right, bottom left, bottom right) and the card classification label.
* A number representing the game the sample belongs to. Samples are in order with full games of backjack represented.

### Data Splits

#### Standard

##### Task classifications

| Class name | Count train | Count val |
| --- | --- | --- |
| hit | 3576 | 1554 |
| stand | 3576 | 1554 |
| surrender | 3576 | 1554 |
| bust | 3576 | 1554 |

##### Concepts

| Concept name | Count train | Count val |
| --- | --- | --- |
| soft | 869 | 325 |
| hard | 13435 | 5891 |
| player_value_21_plus | 3576 | 1554 |
| player_value_21 | 620 | 278 |
| player_value_20 | 714 | 326 |
| player_value_19 | 517 | 220 |
| player_value_18 | 554 | 235 |
| player_value_17 | 621 | 270 |
| player_value_16 | 3994 | 1720 |
| player_value_15 | 724 | 271 |
| player_value_14 | 624 | 245 |
| player_value_13 | 599 | 269 |
| player_value_12 | 591 | 270 |
| player_value_11 | 306 | 165 |
| player_value_10 | 215 | 108 |
| player_value_9 | 192 | 85 |
| player_value_8 | 457 | 200 |
| dealer_card_2 | 735 | 373 |
| dealer_card_3 | 750 | 347 |
| dealer_card_4 | 810 | 317 |
| dealer_card_5 | 791 | 339 |
| dealer_card_6 | 821 | 351 |
| dealer_card_7 | 989 | 343 |
| dealer_card_8 | 901 | 321 |
| dealer_card_9 | 859 | 411 |
| dealer_card_10 | 6119 | 2773 |
| dealer_card_a | 1529 | 641 |
| dealer_multi_cards | 1788 | 778 |

#### Mixed

##### Task classification

| Class name | Count train | Count val |
| --- | --- | --- |
| hit | 3558 | 1550 |
| stand | 3558 | 1550 |
| surrender | 3558 | 1550 |
| bust | 3558 | 1550 |

##### Concepts

| Concept name | Count train | Count val |
| --- | --- | --- |
| soft | 849 | 343 |
| hard | 13383 | 5857 |
| player_value_21_plus | 3558 | 1550 |
| player_value_21 | 621 | 260 |
| player_value_20 | 705 | 308 |
| player_value_19 | 568 | 255 |
| player_value_18 | 542 | 236 |
| player_value_17 | 555 | 240 |
| player_value_16 | 3982 | 1741 |
| player_value_15 | 709 | 286 |
| player_value_14 | 655 | 276 |
| player_value_13 | 617 | 259 |
| player_value_12 | 556 | 277 |
| player_value_11 | 292 | 112 |
| player_value_10 | 219 | 107 |
| player_value_9 | 206 | 92 |
| player_value_8 | 447 | 201 |
| dealer_card_2 | 832 | 349 |
| dealer_card_3 | 787 | 327 |
| dealer_card_4 | 813 | 372 |
| dealer_card_5 | 720 | 358 |
| dealer_card_6 | 774 | 324 |
| dealer_card_7 | 841 | 367 |
| dealer_card_8 | 804 | 388 |
| dealer_card_9 | 875 | 375 |
| dealer_card_10 | 6370 | 2711 |
| dealer_card_a | 1416 | 629 |
| dealer_multi_cards | 1783 | 776 |

## Dataset Creation

### Curation Rationale

This dataset was created to test Concept Bottleneck Models [2] in a human-machine setting.

### Source Data

#### Initial Data Collection and Normalization

The dataset uses background from [3] and playing card images from [4]. The dataset is balanced to the task classification labels. The code used to generate the dataset is available here [5].

### Annotations

#### Annotation process

The annotation process was completed during the generation of the dataset.

#### Who are the annotators?

Annotations were completed by a machine.

### Personal and Sensitive Information

This dataset does not contain personal and sensitive Information.

## Additional Information

### Licensing Information

This dataset is licenced with the [MIT licence](https://choosealicense.com/licenses/mit/).

### Citation Information

[1] Furby, J., Cunnington, D., Braines, D., Preece, A.: Can we constrain concept bottleneck models to learn semantically meaningful input features? (2024), https://arxiv.org/abs/2402.00912

[2] Koh, P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B. &amp; Liang, P.. (2020). Concept Bottleneck Models. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:5338-5348 Available from https://proceedings.mlr.press/v119/koh20a.html.

[3] M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed and A. Vedaldi, "Describing Textures in the Wild," 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3606-3613, doi: 10.1109/CVPR.2014.461.

[4] j4p4n, "Full Deck Of Ornate Playing Cards - English", Available at: https://openclipart.org/download/315253/1550166858.svg

[5] J. Furby, "blackjack-dataset-generator", Available at: https://github.com/JackFurby/blackjack-dataset-generator