Dataset Viewer
Auto-converted to Parquet
Search is not available for this dataset
image
imagewidth (px)
1.02k
1.02k
label
class label
2 classes
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
End of preview. Expand in Data Studio

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

Downloads last month
11