Datasets:
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Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
text string |
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15 0.072747 0.833076 0.049729 0.069241 |
15 0.153366 0.901351 0.051792 0.070399 |
45 0.096254 0.926126 0.053312 0.084427 |
48 0.712106 0.663835 0.059175 0.069241 |
11 0.829859 0.224775 0.049294 0.070914 |
11 0.815255 0.326126 0.052226 0.078764 |
11 0.881650 0.456499 0.055592 0.080051 |
48 0.783605 0.583977 0.056460 0.064736 |
11 0.969544 0.398005 0.050923 0.071943 |
11 0.918187 0.169305 0.050706 0.077349 |
48 0.629262 0.555663 0.058306 0.071171 |
10 0.677470 0.395238 0.057872 0.065380 |
10 0.607763 0.475483 0.053312 0.055470 |
11 0.901086 0.263063 0.055809 0.082625 |
48 0.770738 0.504247 0.057003 0.063835 |
48 0.711346 0.475997 0.032899 0.067053 |
11 0.978882 0.288095 0.042237 0.043887 |
32 0.712975 0.200193 0.040282 0.080952 |
1 0.406352 0.125804 0.045494 0.083012 |
42 0.850923 0.087259 0.032356 0.080566 |
48 0.959772 0.738481 0.029859 0.076190 |
32 0.684799 0.051737 0.035831 0.080566 |
32 0.817101 0.176898 0.034202 0.071686 |
48 0.023398 0.663835 0.046797 0.069241 |
11 0.134962 0.224775 0.049294 0.070914 |
0 0.548969 0.230373 0.045603 0.080566 |
20 0.576384 0.124710 0.035288 0.076448 |
11 0.120358 0.326126 0.052226 0.078764 |
11 0.186754 0.456499 0.055592 0.080051 |
48 0.088708 0.583977 0.056460 0.064736 |
11 0.274647 0.398005 0.050923 0.071943 |
11 0.223290 0.169305 0.050706 0.077349 |
40 0.785668 0.023810 0.034311 0.047619 |
11 0.206189 0.263063 0.055809 0.082625 |
48 0.075841 0.504247 0.057003 0.063835 |
48 0.955809 0.892535 0.026276 0.067954 |
40 0.701412 0.056628 0.013246 0.068211 |
48 0.016450 0.475997 0.032899 0.067053 |
11 0.283985 0.288095 0.042237 0.043887 |
32 0.019110 0.200193 0.038219 0.080952 |
15 0.767644 0.138095 0.049729 0.069241 |
42 0.156026 0.087259 0.032356 0.080566 |
45 0.715201 0.371236 0.051466 0.076834 |
48 0.264875 0.738481 0.029859 0.076190 |
45 0.630022 0.308559 0.051140 0.077091 |
15 0.848263 0.206371 0.051792 0.070399 |
32 0.122204 0.176898 0.034202 0.071686 |
45 0.698969 0.176898 0.047666 0.078121 |
48 0.370033 0.739575 0.031488 0.078121 |
18 0.461238 0.205084 0.036265 0.079408 |
19 0.396471 0.032368 0.035939 0.064736 |
18 0.362758 0.172651 0.036916 0.080952 |
37 0.843268 0.512999 0.051140 0.060232 |
45 0.791151 0.231145 0.053312 0.084427 |
18 0.495657 0.056049 0.037134 0.078636 |
40 0.090771 0.023810 0.034311 0.047619 |
37 0.932465 0.409395 0.050814 0.059974 |
48 0.260912 0.892535 0.026276 0.067954 |
13 0.974864 0.590476 0.050271 0.053539 |
40 0.006569 0.056628 0.013138 0.068211 |
15 0.072747 0.138095 0.049729 0.069241 |
45 0.023018 0.371236 0.046037 0.076834 |
15 0.153366 0.206371 0.051792 0.070399 |
13 0.323996 0.410940 0.051031 0.055341 |
37 0.148371 0.512999 0.051140 0.060232 |
13 0.380293 0.504440 0.056135 0.055470 |
45 0.096254 0.231145 0.053312 0.084427 |
37 0.237568 0.409395 0.050814 0.059974 |
13 0.282790 0.590476 0.055917 0.053539 |
48 0.959772 0.051290 0.029859 0.089833 |
48 0.955809 0.232929 0.026276 0.080121 |
48 0.264875 0.051290 0.029859 0.089833 |
48 0.370033 0.052580 0.031488 0.092109 |
48 0.260912 0.232929 0.026276 0.080121 |
11 0.829859 0.919755 0.049294 0.070914 |
11 0.918187 0.864286 0.050706 0.077349 |
11 0.901086 0.958044 0.055809 0.082625 |
11 0.978882 0.980566 0.042237 0.038867 |
32 0.712975 0.895174 0.040282 0.080952 |
1 0.406352 0.820785 0.045494 0.083012 |
42 0.850923 0.782239 0.032356 0.080566 |
32 0.684799 0.746718 0.035831 0.080566 |
42 0.827253 0.633912 0.033225 0.079151 |
32 0.817101 0.871879 0.034202 0.071686 |
11 0.134962 0.919755 0.049294 0.070914 |
0 0.548969 0.925354 0.045603 0.080566 |
20 0.576384 0.819691 0.035288 0.076448 |
20 0.543702 0.670013 0.043105 0.075933 |
11 0.223290 0.864286 0.050706 0.077349 |
40 0.785668 0.711004 0.034311 0.063192 |
20 0.440879 0.700257 0.036591 0.074389 |
11 0.206189 0.958044 0.055809 0.082625 |
40 0.701412 0.751609 0.013246 0.068211 |
11 0.283985 0.980566 0.042237 0.038867 |
32 0.019110 0.895174 0.038219 0.080952 |
15 0.767644 0.833076 0.049729 0.069241 |
15 0.529316 0.673295 0.033442 0.079151 |
42 0.156026 0.782239 0.032356 0.080566 |
15 0.430293 0.646396 0.033008 0.074003 |
15 0.848263 0.901351 0.051792 0.070399 |
LGD Cards — Day-2 PoC Video (YOLO card-pip dataset)
Auto-labeled playing-card corner-pip detection tiles from the second day of our
proof-of-concept table recordings (Czech Croupier Academy 4K footage), for Live Game Defender
(LGD). This is the day-2 training set behind the current served detector
lgd-cards-gen3 (holdout recall 0.85).
- Format: YOLO —
images/{train,val}+labels/{train,val},data.yaml(52 classes, rank+suit). - ~21,084 tiles, serve-matching tiling.
- Provenance:
boxes.jsonl/manifest.json(per-box origin) +human_corrections.jsonl— this set went through a human-QA pass (14 verified label fixes, incl. a few holdout GT corrections). Labels proposed by a detector, verified/named by an LLM (labeling cost ≈ $8.13). Internal QA review galleries are intentionally not included.
Classes (52)
Standard rank+suit codes (10H, KS, JC, …) — see data.yaml. The detector boxes pips
(~2 per card), not whole cards.
⚠️ Honesty note
Our own PoC / academy recordings, not live casino footage; labels are LLM-verified with a
partial human pass, not casino-grade ground truth (rule of the project: no real-world casino
accuracy claims). Combined with the Public-Domain
Roboflow ow27d v4
base (not redistributed here).
Family
Datasets: day-1 · day-2 (this) · day-3 · holdout. Models: lgd-cards-gen3 (trained on this) · lgd-chips-gen2.
License CC-BY-NC-4.0 (non-commercial, attribution). © 2026 TechTools s.r.o.
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