The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Golden shiners — POLO point-model training and TREx tracking example
A mosaic dataset for a complete tracking workflow: published tracks become point pseudo-annotations, those train a POLO point detector, and TREx tracks with it — or, with no model at all, with its own background subtraction.
Worked end to end in
notebooks/shiners-polo-tracking.ipynb.
What is in it
| File | Size | Contents |
|---|---|---|
shiners-polo.tar.gz |
1.09 GB | dataset.yaml, media_raw/ (3 concatenated recordings), media/frames/kmeans/ (80 frames per recording), tracks/ (the published SchoolTracker tables) |
The POLO training tree is not shipped: it is a rearrangement of the frames and the tables under different filenames, so it would enlarge the download to carry nothing new. Section 2 of the notebook rebuilds it in seconds.
The tables under tracks/ were produced by a converter that lives in
collective-motion-shiners.ipynb,
not inside mosaic, so their src_format is one no installed mosaic knows. Reading
them works; re-converting from source needs that notebook's converter cell.
Contents
shiners-polo.tar.gz unpacks to shiners-polo/, a mosaic dataset:
| Root | What |
|---|---|
media_raw/ |
3 re-encoded H.264 recordings, one per group size (10 / 30 / 70 fish), ~24,600 frames each |
media/frames/ |
80 k-means-selected PNG frames per recording (240 total) |
tracks/ |
the published SchoolTracker tables, identity-corrected |
dataset.yaml |
the manifest |
tar xzf shiners-polo.tar.gz
Then point the notebook's LOCAL_DATASET at shiners-polo/, or let it download this
archive itself.
No model weights
None are included. The trainer, Ultralytics, is AGPL-3.0; rather than work through what distributing a model produced by it requires, this dataset carries none. The notebook's model-free TREx route needs no weights, and its training section prints the command to produce your own.
Provenance and licence
Free-swimming golden shiners (Notemigonus crysoleucas) in a 2.1 x 1.2 m tank, filmed from above, tracked with SchoolTracker and manually identity-corrected.
Source data CC0 1.0 from Dryad, doi:10.5061/dryad.sbcc2fr2h. The recordings here are re-encoded (each trial's segments concatenated, first frame of each segment dropped to match the track tables) and the tables converted to mosaic's schema; both are derivatives of that record and carry the same licence.
Please cite the original work:
Davidson JD, Sosna MMG, Twomey CR, Sridhar VH, Leblanc SP, Couzin ID (2021) Collective detection based on visual information in animal groups. Journal of the Royal Society Interface 18: 20210142. doi:10.1098/rsif.2021.0142
A note on X/Y
In these tables X/Y is the fish's head, not its body centre — SchoolTracker
reports it that way. It matters when training a point model on them, and the notebook
says why.
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