Datasets:
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
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 1859, 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 1694, 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 1880, 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 |
|---|
0 0.48250000 0.80550000 0.09500000 0.22500000 |
1 0.50150000 0.35950000 0.69100000 0.71900000 |
0 0.49000000 0.77250000 0.11000000 0.28500000 |
1 0.50500000 0.36500000 0.74000000 0.73000000 |
0 0.46250000 0.79250000 0.09500000 0.24500000 |
1 0.50000000 0.36750000 0.63000000 0.73500000 |
0 0.46500000 0.77250000 0.10000000 0.28500000 |
1 0.51000000 0.35700000 0.65000000 0.71400000 |
0 0.49000000 0.80500000 0.11000000 0.22400000 |
1 0.51500000 0.37000000 0.66000000 0.68000000 |
0 0.47500000 0.81400000 0.12000000 0.28800000 |
1 0.51000000 0.36500000 0.65000000 0.73000000 |
0 0.61750000 0.30950000 0.10700000 0.14100000 |
1 0.45200000 0.30750000 0.82600000 0.61500000 |
1 0.17250000 0.69000000 0.25500000 0.50000000 |
2 0.26000000 0.75500000 0.14000000 0.35000000 |
2 0.38500000 0.75500000 0.14000000 0.35000000 |
2 0.50750000 0.75500000 0.13500000 0.35000000 |
2 0.63250000 0.75500000 0.13500000 0.35000000 |
2 0.75500000 0.75500000 0.13000000 0.35000000 |
2 0.85500000 0.75500000 0.09000000 0.35000000 |
0 0.80950000 0.30250000 0.05100000 0.12700000 |
1 0.44950000 0.30750000 0.83100000 0.61500000 |
1 0.18300000 0.68500000 0.25200000 0.49400000 |
2 0.28750000 0.74100000 0.12100000 0.34000000 |
2 0.40150000 0.75900000 0.15100000 0.34600000 |
2 0.49700000 0.74850000 0.12000000 0.32500000 |
2 0.59650000 0.74850000 0.13100000 0.32500000 |
2 0.69950000 0.74100000 0.12500000 0.34000000 |
2 0.81050000 0.74850000 0.14300000 0.32500000 |
1 0.45000000 0.32150000 0.83000000 0.64300000 |
1 0.18100000 0.70950000 0.19800000 0.50900000 |
2 0.29750000 0.76100000 0.15500000 0.35000000 |
2 0.43400000 0.75350000 0.14400000 0.36500000 |
2 0.55300000 0.75350000 0.14400000 0.36500000 |
2 0.67000000 0.75350000 0.14400000 0.36500000 |
2 0.83400000 0.75350000 0.14400000 0.36500000 |
2 0.14750000 0.44050000 0.05700000 0.06300000 |
2 0.74250000 0.50600000 0.07700000 0.07200000 |
0 0.62700000 0.36300000 0.11400000 0.22600000 |
1 0.44950000 0.29800000 0.82900000 0.59600000 |
1 0.18350000 0.72750000 0.26100000 0.53900000 |
2 0.22000000 0.76750000 0.08000000 0.36500000 |
2 0.31750000 0.77050000 0.15500000 0.37500000 |
2 0.44000000 0.77050000 0.09000000 0.37500000 |
2 0.54000000 0.77050000 0.15000000 0.37500000 |
2 0.63850000 0.77050000 0.08700000 0.37500000 |
2 0.74750000 0.77050000 0.15500000 0.37500000 |
2 0.88250000 0.77050000 0.13500000 0.37500000 |
2 0.11250000 0.32300000 0.06100000 0.06000000 |
2 0.77850000 0.53700000 0.06300000 0.05800000 |
0 0.58750000 0.27000000 0.07500000 0.16000000 |
1 0.46800000 0.29650000 0.81400000 0.59300000 |
1 0.16250000 0.70500000 0.24100000 0.49400000 |
2 0.20750000 0.76000000 0.08500000 0.36000000 |
2 0.30300000 0.75000000 0.11800000 0.35800000 |
2 0.42100000 0.75000000 0.12400000 0.35800000 |
2 0.53200000 0.75000000 0.11400000 0.35800000 |
2 0.63800000 0.75000000 0.11400000 0.35800000 |
2 0.74400000 0.75000000 0.11400000 0.35800000 |
2 0.85000000 0.75000000 0.11400000 0.35800000 |
2 0.20900000 0.43100000 0.05800000 0.06200000 |
2 0.73750000 0.40800000 0.06500000 0.06000000 |
0 0.50150000 0.44250000 0.09700000 0.25500000 |
1 0.44950000 0.30750000 0.83100000 0.61500000 |
1 0.15600000 0.68900000 0.22800000 0.50200000 |
2 0.19500000 0.73650000 0.07000000 0.33100000 |
2 0.28050000 0.73650000 0.12300000 0.33100000 |
2 0.39050000 0.73650000 0.12300000 0.33100000 |
2 0.50050000 0.73650000 0.12300000 0.33100000 |
2 0.61050000 0.73650000 0.12300000 0.33100000 |
2 0.72050000 0.73650000 0.12300000 0.33100000 |
2 0.83050000 0.73650000 0.12300000 0.33100000 |
2 0.93950000 0.73650000 0.12100000 0.33100000 |
2 0.05300000 0.73200000 0.05800000 0.03600000 |
0 0.52700000 0.50050000 0.10400000 0.20700000 |
1 0.47000000 0.29650000 0.79800000 0.59300000 |
1 0.14100000 0.68800000 0.19800000 0.50000000 |
2 0.26250000 0.76250000 0.13500000 0.35500000 |
2 0.38650000 0.76250000 0.13700000 0.35500000 |
2 0.50000000 0.76250000 0.12000000 0.35500000 |
2 0.60500000 0.76250000 0.12000000 0.35500000 |
2 0.69250000 0.76250000 0.08500000 0.35500000 |
2 0.78500000 0.76250000 0.13000000 0.35500000 |
2 0.88750000 0.76250000 0.10500000 0.35500000 |
2 0.96000000 0.76250000 0.08000000 0.35500000 |
2 0.04900000 0.69950000 0.05800000 0.05100000 |
0 0.55000000 0.31750000 0.14000000 0.20500000 |
1 0.44950000 0.30000000 0.83100000 0.60000000 |
1 0.15250000 0.69500000 0.21500000 0.49000000 |
2 0.25000000 0.74050000 0.11200000 0.33900000 |
2 0.36000000 0.75450000 0.17400000 0.36700000 |
2 0.45600000 0.74050000 0.11200000 0.33900000 |
2 0.56900000 0.75450000 0.17400000 0.36700000 |
2 0.66250000 0.74050000 0.11300000 0.33900000 |
2 0.77500000 0.75450000 0.17400000 0.36700000 |
2 0.86850000 0.74050000 0.11300000 0.33900000 |
2 0.94700000 0.75450000 0.10600000 0.36700000 |
2 0.65500000 0.42750000 0.07000000 0.05500000 |
1 0.44650000 0.30500000 0.82100000 0.61000000 |
End of preview.
Cuphead Qwen Teacher YOLO Dataset v2
Private research dataset for training a three-class local detector that will replace Qwen API annotation over the remaining Cuphead/UniVAM frames.
Because the images are frames from a commercial game, keep this repository private
unless the project owner has independently verified redistribution rights. The
license: other metadata is intentionally conservative and does not grant rights
to the underlying game artwork.
Contents
code/ portable training/evaluation/inference package
data/yolo_teacher_v2/ directly trainable YOLO dataset
provenance/final_teacher/ accepted Qwen records and quality decisions
UPLOAD_INSTRUCTIONS.md upload and target-machine download commands
Dataset statistics:
- 2,500 images: 2,100 train and 400 validation.
- 8,512 boxes: 1,935 player, 3,609 npc, 2,968 attack_object.
- 460 explicit empty-label images, including 455 non-gameplay negatives.
- No invalid boxes and no recording-source leakage between train and validation.
Class mapping:
0 player
1 npc
2 attack_object
The validation split is held out by top-level recording source, rather than random individual frames, to avoid near-duplicate source leakage.
Train
After downloading the repository on the 80GB GPU machine:
cd code
PYTHON_BIN=python3.10 ./setup_env.sh .venv
source .venv/bin/activate
BATCH_SIZE=32 ./run_training_pipeline.sh \
../data/yolo_teacher_v2/data.yaml 0
The official yolo26s.pt checkpoint is downloaded by the pipeline and verified by
SHA256. It is deliberately not redistributed in this dataset repository.
Do not use batch=-1 on an 80GB GPU for this small dataset. It targets roughly 60%
GPU memory and can make the batch so large that each epoch has too few optimizer
updates. Start with batch 32; batch 48 is a secondary experiment.
Provenance
The YOLO export was generated from 2,500 accepted Qwen teacher records. Non-gameplay
records use an explicit empty annotation. The source JSONL and the final quality
manifest are retained under provenance/ so the export can be audited.
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