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Datasets:
Fish916
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CageAwareEggDataset

Tasks:
Object Detection
Image Classification
Tabular Regression
Languages:
English
Tags:
precision-livestock-farming
poultry
robotic-inspection
egg-counting
object-detection
cage-aware-counting
License:
Dataset card Files
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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:    ValueError
Message:      Invalid string class label CageAwareEggDataset@0f22b859e6a73fa129f2e6c0896e04a4b334f123
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 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 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2365, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2282, 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 2162, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1446, 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 1469, 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 1144, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label CageAwareEggDataset@0f22b859e6a73fa129f2e6c0896e04a4b334f123

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CageAwareEggDataset

CageAwareEggDataset is a robotic inspection image dataset for cage-aware sub-cage egg counting in structured layer hen houses.

The dataset supports production-oriented egg monitoring, where egg counts need to be assigned to physical cage units rather than only reported as whole-image detections.

Dataset Description

This dataset contains robotic inspection images collected from a structured layer hen house. It includes:

  • YOLO-format annotations for eggs and cage structural components
  • Sub-cage-level egg-count ground truth
  • Vertical-ID group split metadata for spatial generalization testing
  • Ground-truth audit logs
  • Final result tables for baseline and detector-variant comparisons

Annotation Classes

The object annotation classes are:

  • egg
  • cage_edge
  • cage_roi_row1
  • cage_support

Intended Use

This dataset is intended for research on:

  • Precision livestock farming
  • Robotic inspection in layer hen houses
  • Cage-aware sub-cage egg counting
  • Object detection and counting in structured agricultural environments
  • Spatial generalization evaluation using vertical-ID group splits

Validation Protocols

The dataset includes two validation protocols:

  1. Image-level random validation
  2. Vertical-ID group split for testing spatial generalization to withheld cage positions

Data Availability

The full image archive is being uploaded. Metadata, split files, audit logs, example images, and result tables will be provided in this repository.

Citation

If you use this dataset, please cite the associated conference submission:

Cage-Aware Sub-Cage Egg Counting for Robotic Monitoring in Layer Hen Houses
Fei Shi and Weihong Ma, EPA 2026 submission.

License

This dataset is released under the CC BY 4.0 license.
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