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 0.13550000 0.81450000 0.06500000 0.20100000 |
1 0.30750000 0.30900000 0.61500000 0.61800000 |
0 0.25650000 0.82750000 0.07700000 0.18300000 |
1 0.25600000 0.33200000 0.51200000 0.66400000 |
2 0.39300000 0.67250000 0.07200000 0.10900000 |
1 0.85550000 0.51800000 0.28300000 0.46400000 |
2 0.28300000 0.63400000 0.05400000 0.09600000 |
2 0.68500000 0.75200000 0.08400000 0.08600000 |
0 0.77400000 0.67650000 0.07200000 0.06300000 |
1 0.85350000 0.53850000 0.29300000 0.58300000 |
1 0.28000000 0.67700000 0.05400000 0.10000000 |
2 0.27500000 0.62500000 0.06400000 0.11400000 |
0 0.78650000 0.67550000 0.06100000 0.06500000 |
1 0.85700000 0.49450000 0.28600000 0.51100000 |
1 0.25700000 0.70050000 0.07200000 0.09900000 |
2 0.24550000 0.61400000 0.05500000 0.09200000 |
0 0.77250000 0.68000000 0.06900000 0.05800000 |
1 0.82850000 0.46300000 0.34300000 0.53800000 |
2 0.18750000 0.67100000 0.06300000 0.10400000 |
2 0.67150000 0.43800000 0.06300000 0.10600000 |
1 0.87600000 0.45800000 0.24600000 0.54400000 |
1 0.66700000 0.44600000 0.08000000 0.25000000 |
1 0.10800000 0.66250000 0.06800000 0.10300000 |
2 0.17300000 0.62900000 0.06800000 0.10600000 |
2 0.63000000 0.51400000 0.06800000 0.11400000 |
2 0.63550000 0.38750000 0.05700000 0.13900000 |
2 0.63550000 0.62650000 0.05700000 0.11700000 |
0 0.76050000 0.59950000 0.09300000 0.11300000 |
1 0.86550000 0.46100000 0.26900000 0.50600000 |
1 0.59100000 0.46400000 0.06000000 0.28600000 |
1 0.66750000 0.63250000 0.05700000 0.09300000 |
1 0.06750000 0.63950000 0.05700000 0.09300000 |
2 0.07500000 0.62350000 0.06400000 0.11100000 |
2 0.57650000 0.48050000 0.06100000 0.26100000 |
2 0.64450000 0.62500000 0.06100000 0.10000000 |
0 0.78700000 0.56950000 0.09800000 0.11100000 |
1 0.87900000 0.48800000 0.24200000 0.54800000 |
2 0.03250000 0.63050000 0.06500000 0.12500000 |
2 0.53000000 0.53900000 0.07000000 0.33600000 |
2 0.66750000 0.76750000 0.07500000 0.10700000 |
0 0.74950000 0.59200000 0.08700000 0.10800000 |
1 0.86850000 0.51800000 0.26100000 0.55400000 |
1 0.48250000 0.48150000 0.05900000 0.30300000 |
2 0.66500000 0.84000000 0.05600000 0.09000000 |
1 0.85650000 0.53750000 0.28500000 0.51500000 |
1 0.43350000 0.49600000 0.06100000 0.32000000 |
2 0.64450000 0.83850000 0.05300000 0.08700000 |
0 0.77650000 0.57500000 0.08100000 0.16200000 |
1 0.86900000 0.53550000 0.26200000 0.47900000 |
1 0.37800000 0.48850000 0.06600000 0.33500000 |
2 0.39250000 0.48600000 0.05700000 0.33600000 |
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 0.48250000 0.77650000 0.13500000 0.27700000 |
1 0.80650000 0.45750000 0.27700000 0.64500000 |
2 0.23750000 0.41350000 0.04500000 0.13100000 |
2 0.28750000 0.50050000 0.03500000 0.13100000 |
2 0.64000000 0.68250000 0.09000000 0.10500000 |
2 0.57000000 0.84000000 0.08000000 0.09000000 |
2 0.64750000 0.37250000 0.06500000 0.08500000 |
0 0.49750000 0.77250000 0.12500000 0.28500000 |
1 0.83550000 0.50550000 0.32900000 0.77500000 |
2 0.57750000 0.75600000 0.12300000 0.09600000 |
2 0.68900000 0.24100000 0.10000000 0.19600000 |
0 0.47400000 0.77000000 0.11200000 0.27000000 |
1 0.83550000 0.50900000 0.32900000 0.76800000 |
2 0.60100000 0.67700000 0.07800000 0.08400000 |
0 0.46500000 0.78000000 0.10000000 0.22000000 |
1 0.83000000 0.49500000 0.31000000 0.73000000 |
2 0.65500000 0.18500000 0.15400000 0.25000000 |
0 0.29950000 0.77500000 0.17100000 0.16400000 |
1 0.81450000 0.45850000 0.28700000 0.75300000 |
2 0.44750000 0.31650000 0.16700000 0.31700000 |
0 0.19150000 0.73350000 0.11300000 0.18700000 |
1 0.82700000 0.47750000 0.29000000 0.70500000 |
2 0.29500000 0.73850000 0.13800000 0.33500000 |
0 0.09950000 0.77650000 0.11500000 0.16700000 |
1 0.83400000 0.47650000 0.27600000 0.71700000 |
2 0.57650000 0.34150000 0.02700000 0.04700000 |
0 0.11050000 0.79450000 0.10700000 0.22300000 |
1 0.83550000 0.50000000 0.32900000 0.70000000 |
2 0.61300000 0.31550000 0.06000000 0.09100000 |
0 0.05750000 0.83250000 0.11500000 0.19500000 |
2 0.27250000 0.75000000 0.06700000 0.07200000 |
2 0.57950000 0.20000000 0.03300000 0.07800000 |
2 0.64650000 0.59800000 0.03300000 0.05400000 |
2 0.85450000 0.57150000 0.03700000 0.05700000 |
2 0.82800000 0.68900000 0.03000000 0.05000000 |
0 0.03250000 0.82500000 0.06500000 0.18000000 |
1 0.86600000 0.60700000 0.26800000 0.50600000 |
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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