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Datasets:
JackFurby
/
playing-cards

Tasks:
Image Classification
Image Segmentation
Sub-tasks:
multi-label-image-classification
instance-segmentation
Size:
10K<n<100K
Tags:
attribute
concepts
License:
Dataset card Files Community
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'list' object", 'Conversion failed for column 0 with type object')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column(/0/card_points/[]/[]) changed from array to number in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 167, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                File "pyarrow/table.pxi", line 3874, in pyarrow.lib.Table.from_pandas
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 611, in dataframe_to_arrays
                  arrays = [convert_column(c, f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 611, in <listcomp>
                  arrays = [convert_column(c, f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 598, in convert_column
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 592, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 339, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 85, in pyarrow.lib._ndarray_to_array
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'list' object", 'Conversion failed for column 0 with type object')

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Dataset Card for Playing cards

Dataset Description

Dataset Summary

A dataset containing four sets of playing card images. Each set contains 10,000 images and has a series of attributes. Cards are randomly rotated, flipped and scaled (within limits).

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:

  • A set number of playing cards in each sample
  • 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,
    'concept_label': list of 0s and 1s,
    'class_label': integer,
    'card_points': list of tuples and card class labels as integers
}

Single

Single playing card on a random background.

  • Number of playing cards: 1
  • Concepts: Suit and rank
  • Class label: Card classification
  • Card points: Coordinates of the card and card classification
Example 
1599, {
    'img_path': 'imgs/single/1599.png',
    'concept_label': [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
    'class_label': 11,
    'card_points': [[[(657, 517), (405, 609), (520, 139), (268, 231)], 11]]
}

Three

Three randomly selected playing cards on a random background. Class label is set to hand rank for the game Three card poker.

  • Number of playing cards: 3
  • Concepts: Cards present
  • Class label: Hand rank
  • Card points: Coordinates of the cards and card classifications
Example 
5159, {
    'img_path': 'imgs/three/5159.png',
    'concept_label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'class_label': 4,
    'card_points': [[[(35, 517), (68, 374), (250, 567), (283, 424)], 15], [[(70, 364), (103, 221), (285, 413), (318, 270)], 24], [[(106, 210), (139, 67), (321, 260), (354, 117)], 13]]
}

Three card poker

Three playing cards on a random background. Class label is set to hand rank for the game Three card poker.

  • Number of playing cards: 3
  • Concepts: Cards present
  • Class label: Hand rank
  • Card points: Coordinates of the cards and card classifications
Example 
9259, {
    'img_path': 'imgs/three_card_poker/9259.png',
    'concept_label': [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    'class_label': 4,
    'card_points': [[[(42, 478), (84, 347), (237, 541), (279, 411)], 8], [[(325, 271), (282, 401), (129, 208), (87, 338)], 13], [[(370, 132), (328, 262), (175, 68), (133, 198)], 10]]
}

Class-level Three card poker

Three playing cards on a random background. Class label is set to hand rank for the game Three card poker. This set of samples have concepts set to the class. Every instance of the same class will have the same concept vector.

  • Number of playing cards: 3
  • Concepts: Cards present
  • Class label: Hand rank
  • Card points: Coordinates of the cards and card classifications
Example 
5992, {
    'img_path': 'imgs/three_card_poker_class_level/5992.png',
    'concept_label': [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1],
    'class_label': 5,
    'card_points': [[[(539, 98), (574, 247), (317, 150), (351, 298)], 5], [[(388, 457), (354, 309), (610, 406), (576, 257)], 10], [[(613, 416), (647, 565), (390, 468), (425, 616)], 7]]
}

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. 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, either corresponding to concepts.txt for images with three cards per image or classes.txt for images with a single card present.

Data Splits

Single

Task classifications
Class name Count train Count val
2C 135 58
2D 135 58
2H 135 58
2S 135 58
3C 135 58
3D 135 58
3H 135 58
3S 135 58
4C 135 58
4D 135 58
4H 135 58
4S 135 58
5C 135 58
5D 135 58
5H 135 58
5S 135 58
6C 134 58
6D 134 58
6H 134 58
6S 134 58
7C 134 58
7D 134 58
7H 134 58
7S 134 58
8C 134 58
8D 134 58
8H 134 58
8S 134 58
9C 134 58
9D 134 58
9H 134 58
9S 134 58
10C 134 58
10D 134 58
10H 134 58
10S 134 58
JC 134 58
JD 134 58
JH 134 58
JS 134 58
QC 134 58
QD 134 58
QH 134 58
QS 134 58
KC 134 58
KD 134 58
KH 134 58
KS 134 58
Concepts
Concept name Count train Count val
2 540 232
3 540 232
4 540 232
5 540 232
6 536 232
7 536 232
8 536 232
9 536 232
10 536 232
J 536 232
Q 536 232
K 536 232
A 536 232
C 1746 754
D 1746 754
H 1746 754
S 1746 754

Three

Task classification
Class name Count train Count val
straight_flush 20 2
three_of_a_kind 17 11
straight 268 99
flush 332 149
pair 1171 524
high_card 5191 2216
Concepts
Concept name Count train Count val
2C 398 181
2D 441 161
2H 385 143
2S 397 170
3C 439 171
3D 383 165
3H 398 181
3S 435 179
4C 407 164
4D 402 179
4H 409 168
4S 403 191
5C 402 150
5D 373 173
5H 383 187
5S 426 178
6C 394 176
6D 414 172
6H 398 184
6S 413 163
7C 409 171
7D 412 158
7H 391 185
7S 453 176
8C 390 171
8D 398 171
8H 406 148
8S 368 193
9C 381 187
9D 429 167
9H 391 193
9S 370 174
10C 450 171
10D 420 161
10H 436 180
10S 406 169
JC 416 160
JD 411 176
JH 409 182
JS 404 178
QC 403 171
QD 376 185
QH 407 182
QS 420 156
KC 414 180
KD 384 176
KH 377 157
KS 382 192
AC 348 168
AD 408 177
AH 427 174
AS 401 178

Three card poker

Task classification
Class name Count train Count val
straight_flush 1166 501
three_of_a_kind 1166 501
straight 1166 501
flush 1166 501
pair 1166 500
high_card 1166 500
Concepts
Concept name Count train Count val
2C 344 171
2D 368 181
2H 386 161
2S 359 163
3C 400 186
3D 421 181
3H 414 181
3S 407 172
4C 409 185
4D 388 199
4H 408 185
4S 411 195
5C 403 176
5D 409 191
5H 401 198
5S 392 206
6C 422 177
6D 405 194
6H 444 161
6S 412 175
7C 420 181
7D 404 189
7H 429 156
7S 433 158
8C 422 173
8D 436 159
8H 412 167
8S 431 166
9C 407 186
9D 405 174
9H 432 169
9S 398 185
10C 416 164
10D 413 174
10H 430 171
10S 402 176
JC 431 170
JD 462 158
JH 443 145
JS 405 186
QC 401 186
QD 432 163
QH 419 187
QS 397 172
KC 363 157
KD 358 178
KH 367 157
KS 364 155
AC 380 155
AD 363 151
AH 359 150
AS 351 156

Class-level Three card poker

Task classification
Class name Count train Count val
straight_flush 1166 501
three_of_a_kind 1166 501
straight 1166 501
flush 1166 501
pair 1166 500
high_card 1166 500
Concepts
Concept name Count train Count val
2H 1166 501
3H 2332 1002
4C 2332 1002
4D 2332 1002
4H 1166 501
4S 2332 1001
5C 1166 500
5D 3498 1501
6D 1166 501
9D 1166 501
10H 2332 1000

Dataset Creation

Curation Rationale

This dataset was created to test Concept Bottleneck Models [1] with instance and class level concepts.

Source Data

Initial Data Collection and Normalization

The dataset uses background from [2] and playing card images from [3]. The dataset is balanced to the task classification labels with concepts, backgrounds and card transformations being applied randomly. The code used to generate the dataset is available here [4].

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] 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.

[2] 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.

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

[4] J. Furby, "playing-card-concept-generator", Available at: https://github.com/JackFurby/playing-card-concept-generator

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