The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 306, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Project3-1: complete training backup
Private research backup under David's account. The archive contains original CamVid images/annotations, prepared greyscale images and labels, block labels, training flips, all three coefficient levels, final and smoke/pilot runs, checkpoints, logs, configs, evaluation counts, reports and exact source snapshots. CamVid remains subject to its original terms; this copy does not relicense it.
ARCHIVE.json gives the archive SHA-256 and size. MANIFEST.json gives every
payload file's SHA-256 and size. The completed 18-run CUDA study is inside
runs/vast-60293344/; other run folders are explicitly pilot/smoke evidence.
Final runs include validation-selected best.pt, resumable last.pt, fitted
normalization, training histories and per-frame evaluation counts.
Download and restore
hf auth login
hf download davidwickerhf/project31-aeveon-training --repo-type dataset --local-dir project31-backup
cd project31-backup
mkdir restored
tar -xzf project31-full-backup.tar.gz -C restored
cd restored
mkdir code
tar -xzf source/project31-current.tar.gz -C code
cd code
python3 -m venv .venv
source .venv/bin/activate
pip install -e '.[analysis,dev]'
export SEGDECODE_DATA="$(cd ../data && pwd)"
python scripts/evaluate.py ../runs/vast-60293344/haar_stem/medium/seed0 --root "$SEGDECODE_DATA" --device cpu
Use source/CURRENT_COMMIT for the backed-up checkout revision and
source/deployed/ for the exact main/control GPU training sources. Saved training
configs retain original remote data paths; resuming requires restoring those
paths and the original source/backend/configuration. No Hugging Face compute job
is started by this repository.
Three-seed mean test mIoU: ENet 53.58%; raw pixels 45.42%; standardized pixels
45.13%; Haar light/medium/heavy 48.67% / 47.28% / 45.82%. The small stems use
about 32.1% of ENet's arithmetic cost. Combined rule-based refinement recovers
only 1.50% of the ENet gap at a 20% context-inclusive decoding cap. Full results
and methodological limitations are in reports/study/ inside the archive.
Source repository: https://github.com/ebputnina/Project3-1/tree/dev
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