The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label cattle-bcs-dge@2efb0c90487f5eca7693457d20bd70871be02a8d
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
example_data = self.str2int(example_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
output = [self._strval2int(value) for value in values]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label cattle-bcs-dge@2efb0c90487f5eca7693457d20bd70871be02a8dNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Criollo Cattle Body Condition Score (DGE Images)
A redistribution of the pre-extracted, pre-sorted image subset of the Winkler &
Boucheron Criollo-cattle body-condition-score (BCS) dataset, mirrored here so a
course notebook can fetch it programmatically with hf_hub_download. The
original Dryad release is gated behind an anti-scraping wall that blocks
automated downloads, which this mirror exists to work around.
Provenance
- Original dataset: Winkler, Z. & Boucheron, L. Depth + RGB Videos of Criollo Cattle for Body Condition Scoring. Dryad.
- DOI: 10.5061/dryad.tqjq2bw4s
- License: CC0 1.0 (public domain dedication), which permits this redistribution without restriction.
- Source code: github.com/zwinkler/Automated-BCS
Only the 170 MB Total_sorted_DGE_images.zip is mirrored here. The original
34 GB of raw RGB and depth ROS bag videos are not, since the pre-extracted
images are what the notebook trains on.
What "DGE" Means
Each .tif is a 224x224, three-channel, uint8 image, but the channels are not
RGB. They are a Depth + Grayscale + Edge composite extracted from the depth
camera, so channel 0 is depth, channel 1 is grayscale, and channel 2 is an edge
map. Models pretrained on natural RGB photographs therefore transfer across a
domain gap, which is worth noting in any analysis.
Structure
Total_sorted_DGE_images/<bcs_grade>/Cow_<id>/<frame>_DGE.tif
The BCS grade is the top-level folder, the cow identifier is the next level, and frames sit beneath each cow. The label comes from the folder, not the filename, and each animal's grade is the mode of multiple judges' scores, so inter-rater disagreement is built into the ground truth.
Class Distribution
The grades run 1 to 9, but the usable mass sits in the middle, reflecting a herd of range Criollo cattle that skews lean. Counts measured from the archive:
| BCS grade | Images | Animals |
|---|---|---|
| 2 | 552 | 5 |
| 3 | 555 | 18 |
| 4 | 2261 | 48 |
| 5 | 2015 | 52 |
| 6 | 692 | 24 |
| 7 | 19 | 1 |
| 1, 8, 9 | 1 each | <1 |
The sparse tails make a fine-grained 9-way problem impractical, so a consuming project is expected to coerce the grades into a smaller set of condition buckets and address the residual imbalance, rather than treating all nine grades as trainable classes.
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