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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:    ArrowInvalid
Message:      Could not open Parquet input source '<Buffer>': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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/parquet/parquet.py", line 205, in _generate_tables
                  if parquet_fragment.row_groups:
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/_dataset_parquet.pyx", line 389, in pyarrow._dataset_parquet.ParquetFileFragment.row_groups.__get__
                File "pyarrow/_dataset_parquet.pyx", line 396, in pyarrow._dataset_parquet.ParquetFileFragment.metadata.__get__
                  self.ensure_complete_metadata()
                File "pyarrow/_dataset_parquet.pyx", line 385, in pyarrow._dataset_parquet.ParquetFileFragment.ensure_complete_metadata
                  check_status(self.parquet_file_fragment.EnsureCompleteMetadata())
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Could not open Parquet input source '<Buffer>': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.

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Efficientmaize Classification

A dataset for quality classification of maize. The dataset contains raw and augmented versions.
The raw dataset contains 4,846 images.
Images per class:

  • Bad: 2,211
  • Good: 2,635

The augmented dataset contains 28,899 images.
Images per class:

  • Bad: 13,246
  • Good: 15,653

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

@article{asante2024efficientmaize,
  title={EfficientMaize: A lightweight dataset for maize classification on resource-constrained devices},
  author={Asante, Emmanuel and Appiah, Obed and Appiahene, Peter and Adu, Kwabena},
  journal={Data in Brief},
  volume={54},
  pages={110261},
  year={2024},
  publisher={Elsevier}
}

Asante, Emmanuel ; Appiah, Obed; APPIAHENE, PETER (2023), “Lightweight Dataset for Maize Classification on Resource-Constrained Devices”, Mendeley Data, V2, doi: 10.17632/r6vvm5jkh6.2

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