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Dataset Card for Blackjack
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 [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.
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. & 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
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