Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
threshold: double
precision_on_test_set: double
recall: double
f1: double
roc_auc: double
criterion: string
see_also: string
_description: string
provenance: string
model: string
effective_fps: double
min_inter_event_sec: double
split_oversized_events_note: string
gap_frames: int64
min_event_sec: double
split_oversized_events: bool
effective_fps_note: string
measured_performance: struct<scope: string, recall: double, precision: double, f1: double, f2: double, n_marks: int64>
child 0, scope: string
child 1, recall: double
child 2, precision: double
child 3, f1: double
child 4, f2: double
child 5, n_marks: int64
max_event_sec: double
generalization_check: struct<note: string, 2025-08-09_only: struct<recall: double, precision: double, f2: double>, 2025-08 (... 64 chars omitted)
child 0, note: string
child 1, 2025-08-09_only: struct<recall: double, precision: double, f2: double>
child 0, recall: double
child 1, precision: double
child 2, f2: double
child 2, 2025-08-08_only: struct<recall: double, precision: double, f2: double>
child 0, recall: double
child 1, precision: double
child 2, f2: double
locked_date: timestamp[s]
gap_sec: double
to
{'_description': Value('string'), 'model': Value('string'), 'threshold': Value('float64'), 'min_event_sec': Value('float64'), 'max_event_sec': Value('float64'), 'min_inter_event_sec': Value('float64'), 'gap_frames': Value('int64'), 'gap_sec': Value('float64'), 'effective_fps': Value('float64'), 'effective_fps_note': Value('string'), 'split_oversized_events': Value('bool'), 'split_oversized_events_note': Value('string'), 'provenance': Value('string'), 'measured_performance': {'scope': Value('string'), 'recall': Value('float64'), 'precision': Value('float64'), 'f1': Value('float64'), 'f2': Value('float64'), 'n_marks': Value('int64')}, 'generalization_check': {'note': Value('string'), '2025-08-09_only': {'recall': Value('float64'), 'precision': Value('float64'), 'f2': Value('float64')}, '2025-08-08_only': {'recall': Value('float64'), 'precision': Value('float64'), 'f2': Value('float64')}}, 'locked_date': Value('timestamp[s]')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
threshold: double
precision_on_test_set: double
recall: double
f1: double
roc_auc: double
criterion: string
see_also: string
_description: string
provenance: string
model: string
effective_fps: double
min_inter_event_sec: double
split_oversized_events_note: string
gap_frames: int64
min_event_sec: double
split_oversized_events: bool
effective_fps_note: string
measured_performance: struct<scope: string, recall: double, precision: double, f1: double, f2: double, n_marks: int64>
child 0, scope: string
child 1, recall: double
child 2, precision: double
child 3, f1: double
child 4, f2: double
child 5, n_marks: int64
max_event_sec: double
generalization_check: struct<note: string, 2025-08-09_only: struct<recall: double, precision: double, f2: double>, 2025-08 (... 64 chars omitted)
child 0, note: string
child 1, 2025-08-09_only: struct<recall: double, precision: double, f2: double>
child 0, recall: double
child 1, precision: double
child 2, f2: double
child 2, 2025-08-08_only: struct<recall: double, precision: double, f2: double>
child 0, recall: double
child 1, precision: double
child 2, f2: double
locked_date: timestamp[s]
gap_sec: double
to
{'_description': Value('string'), 'model': Value('string'), 'threshold': Value('float64'), 'min_event_sec': Value('float64'), 'max_event_sec': Value('float64'), 'min_inter_event_sec': Value('float64'), 'gap_frames': Value('int64'), 'gap_sec': Value('float64'), 'effective_fps': Value('float64'), 'effective_fps_note': Value('string'), 'split_oversized_events': Value('bool'), 'split_oversized_events_note': Value('string'), 'provenance': Value('string'), 'measured_performance': {'scope': Value('string'), 'recall': Value('float64'), 'precision': Value('float64'), 'f1': Value('float64'), 'f2': Value('float64'), 'n_marks': Value('int64')}, 'generalization_check': {'note': Value('string'), '2025-08-09_only': {'recall': Value('float64'), 'precision': Value('float64'), 'f2': Value('float64')}, '2025-08-08_only': {'recall': Value('float64'), 'precision': Value('float64'), 'f2': Value('float64')}}, 'locked_date': Value('timestamp[s]')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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sea-turtle-assets
Public assets for the sea turtle behavior pipeline.
Contents
| Path | Description |
|---|---|
models/yolo/best.pt |
YOLO11m-seg model — sea turtle detection |
models/yolo/inference_config.json |
Calibrated conf/iou/max_det this checkpoint needs — see below |
models/breathing/best_model.keras |
ResNet50 classifier - breathing detection |
models/breathing/optimal_threshold.json |
F1-optimal threshold for breathing classifier |
1min_sample/ |
1-minute sample clips (2 cameras, 2 days) for pipeline testing |
Downloaded automatically by setup.sh -- no manual action needed.
YOLO inference settings
Always run this model with max_det=1 — every tank holds exactly one turtle,
so this is a domain constraint, not a tuning choice. Use inference_config.json's
conf (0.40) rather than a generic default: it's calibrated against the full
validation set under max_det=1, the model's actual deployment shape, and
holds P=R=F1=1.0 across a wide safe range (0.05-0.75) rather than sitting at
either edge of it.
2026-09-05: replaced the previous checkpoint, which scored well on paper (fitness in the top 10 of ~40 sweep runs) but had 89 false positives and 80% recall when actually run at real inference settings. Root cause was two independent bugs — a checkpoint-selection bug in training.py that could save the wrong epoch's weights as best.pt, and 2 of 50 validation images carrying duplicate ground-truth labels — both since fixed. This checkpoint is a full retrain under the corrected code and corrected labels, verified with a clean P=R=F1=1.0 sweep on the full val set.
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