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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<model.11.gamma: double, model.11.attention_mean: double, model.11.attention_std: double, model.11.attention_min: double, model.11.attention_max: double, model.11.attention_p10: double, model.11.attention_p25: double, model.11.attention_p50: double, model.11.attention_p75: double, model.11.attention_p90: double, model.12.gamma: double, model.12.attention_mean: double, model.12.attention_std: double, model.12.attention_min: double, model.12.attention_max: double, model.12.attention_p10: double, model.12.attention_p25: double, model.12.attention_p50: double, model.12.attention_p75: double, model.12.attention_p90: double, model.13.gamma: double, model.13.attention_mean: double, model.13.attention_std: double, model.13.attention_min: double, model.13.attention_max: double, model.13.attention_p10: double, model.13.attention_p25: double, model.13.attention_p50: double, model.13.attention_p75: double, model.13.attention_p90: double>
to
{}
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 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<model.11.gamma: double, model.11.attention_mean: double, model.11.attention_std: double, model.11.attention_min: double, model.11.attention_max: double, model.11.attention_p10: double, model.11.attention_p25: double, model.11.attention_p50: double, model.11.attention_p75: double, model.11.attention_p90: double, model.12.gamma: double, model.12.attention_mean: double, model.12.attention_std: double, model.12.attention_min: double, model.12.attention_max: double, model.12.attention_p10: double, model.12.attention_p25: double, model.12.attention_p50: double, model.12.attention_p75: double, model.12.attention_p90: double, model.13.gamma: double, model.13.attention_mean: double, model.13.attention_std: double, model.13.attention_min: double, model.13.attention_max: double, model.13.attention_p10: double, model.13.attention_p25: double, model.13.attention_p50: double, model.13.attention_p75: double, model.13.attention_p90: double>
              to
              {}

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Check out the documentation for more information.

Varroa: three YOLOv8-P2 ASRM variants

Standalone package for training the three current ASRM architectures on the Varroa dataset. It is isolated from LEVIR-Ship and from the original Varroa project.

Cases:

p2_asrm_fusion
input_asrm_auxiliary
input_asrm_guided_p2

The notebook follows Varroa/YOLO_custom/HF_YOLO_version_workflow.ipynb: gt_one, infected images only, class token 3 mapped to class 1, fixed conversion split seed 42, pretrained yolov8n.pt, image size 640, batch 16, 100 epochs, patience 20, workers 4 and training seeds 42/43.

The raw archive retains the original train/val/test layout. prepare_dataset.py deduplicates the positive CSV records and creates a reproducible 70/15/15 Ultralytics layout. All cases and training seeds use that same converted dataset.

Outputs:

test_metrics.json per case/seed;
comparison_varroa_asrm.csv with raw runs;
comparison_varroa_asrm_aggregate.csv with mean/std over seeds.

Version 2 exposes an auxiliary attention map but has no auxiliary loss, so its detection path is intentionally equivalent to the P2 baseline during this experiment.

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