Datasets:

SmartFarmingLab
/

corn1000

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 212, in _split_generators
                  raise ValueError(
                      f"Metadata files {downloaded_metadata_file} and {features_per_metadata_file[0][0]} have different features: {features_per_metadata_file[0]} != {metadata_features}"
                  )
              ValueError: Metadata files hf://datasets/SmartFarmingLab/corn1000@fcb560b8841665a9f6c95a83892220e809ac5c9f/data/test/metadata.jsonl and hf://datasets/SmartFarmingLab/corn1000@fcb560b8841665a9f6c95a83892220e809ac5c9f/data/train/metadata.jsonl have different features: ('hf://datasets/SmartFarmingLab/corn1000@fcb560b8841665a9f6c95a83892220e809ac5c9f/data/train/metadata.jsonl', {'count': Value('int64'), 'arrangement_id': Value('string'), 'session': Value('string'), 'step': Value('int64'), 'take': Value('int64'), 'species': Value('string'), 'pollution': Value('string'), 'camera': Value('string'), 'taken_at': Value('string'), 'file_name': Value('string'), 'raw_file': Value('string'), 'kernel_points': List(List(Value('float64'))), 'broken_points': List(List(Value('float64'))), 'invalid_points': List(List(Value('float64')))}) != {'count': Value('int64'), 'arrangement_id': Value('string'), 'session': Value('string'), 'step': Value('int64'), 'take': Value('int64'), 'species': Value('string'), 'pollution': Value('string'), 'camera': Value('string'), 'taken_at': Value('string'), 'file_name': Value('string'), 'raw_file': Value('string'), 'kernel_points': Value('null'), 'broken_points': Value('null'), 'invalid_points': Value('null')}
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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corn1000 — counted cereal kernels on A4

Photos of cereal kernels spread on a white A4 sheet, each labelled with the exact number of kernels on the sheet. The dataset is built to train models that count kernels from a single phone photo, as the first step of estimating the thousand-kernel weight (TKW = mass ÷ count × 1000). A 1 % count error is a 1 % TKW error, so the collection protocol is built around label correctness.


Images	836
Arrangements (distinct kernel layouts)	128
Kernels per image	170 – 1000
Images with point annotations	158

Session	Species	Pollution	Cameras	Images	Arrangements	Kernels
S02	oats-s	none	Galaxy S23, iPhone 8	124	31	240–798
S03	barley-w	light	Galaxy S23, Galaxy Tab S6 Lite, iPhone 8	231	28	225–733
S04	wheat-w	light	Galaxy Tab S6 Lite, Pixel 9	131	33	170–800
S05	durum-w	light	Galaxy S23, iPhone 8	265	31	230–782
count_1000	barley-w, durum-w, rye-w, wheat-w, oats-s	light, none	Galaxy S23, iPhone 8	85	5	1000–1000

Fields

Column	Meaning
`image`	The photo. JPEG; see Orientation below.
`count`	Number of kernels on the sheet — the label.
`arrangement_id`	One physical layout of kernels (`<session>-<step>[-<take>]`). Every arrangement is photographed several times, see Splitting.
`session`, `step`, `take`	Session ID, card number within the session, and re-shoot number after a REDO.
`species`	`rye-w` winter rye, `durum-w` winter durum wheat, `barley-w` winter barley, `oats-s` spring oats, `wheat-w` winter soft wheat.
`pollution`	`none`, `light` or `heavy`: amount of chaff, awns, broken kernels, pebbles, dust or office materials mixed in. Never counted.
`camera`	Camera model from EXIF.
`taken_at`	Capture time from EXIF (camera clock, no time zone).
`raw_file`	Path of the original raw file in this repo (`raw/…`) if the photo was shot as DNG; else empty.
`kernel_points`	`[x, y]` of every counted kernel, see Point annotations; null if the photo is not annotated.
`broken_points`	`[x, y]` of broken kernels (not counted); null if not annotated.
`invalid_points`	`[x, y]` of other objects marked as not countable; null if not annotated.

How it was collected

Counting rules

Based on ISO 520:2010.

Counts as a kernel: an intact kernel, including shrivelled and small ones. Barley and oats count with their husk. Twins count as two kernels.

Never counts: broken kernels; chaff, straw and awns; foreign seeds, dust and stones. Borderline cases were sorted out rather than judged, to keep the count exact.

Pollution in the data: chaff, awns, broken kernels, pebbles, dust or office materials will occur in the use case, so some sessions deliberately contain them as noise (pollution = light or heavy). They are never part of count.

Materials

White A4 sheet on a dark or high-contrast background
Cups for the kernels, each holding a verified 50 kernels
Printed session cards and special cards (REDO, INVALIDATE) from the planning script

Sessions

S02: spring oats (oats-s), not polluted; Galaxy S23 and iPhone 8; 2026-10-02.
S03: winter barley (barley-w), lightly polluted; Galaxy S23 (shot as DNG), iPhone 8 and Galaxy Tab S6 Lite; shot over three days, 2026-09-16 to 09-21.
S04: winter soft wheat (wheat-w), lightly polluted; Pixel 9 and Galaxy Tab S6 Lite; 2026-10-15. The tablet missed or misdated the photos of cards 0, 3, 8, 14 and 15; its photos of those layouts were placed on the card by hand and checked against the Pixel photos.
S05: winter durum wheat (durum-w), lightly polluted; Galaxy S23 (shot as DNG) and iPhone 8; 2026-10-15.
count_1000: the test split. All five species with exactly 1000 kernels in every photo, 13–21 photos per species; barley lightly polluted, the others not polluted; Galaxy S23 (shot as DNG) and iPhone 8; 2026-10-26. No session cards were photographed: the species were assigned by capture time and checked by eye, and each species is one arrangement_id.

Splitting

Images are not independent: every arrangement appears in 2–22 photos (several phones, several angles), and consecutive arrangements of a session share most of their kernels. Split by session; never by image, and never by arrangement_id alone.

The test split is session count_1000 (see Sessions); train holds all other sessions.

Loading

from datasets import load_dataset

ds = load_dataset("SmartFarmingLab/corn1000")

Orientation: most JPEGs are stored sideways with an EXIF orientation tag, exactly as the phones wrote them. datasets, browsers and cv2.imread apply the tag; plain PIL.Image.open does not — call PIL.ImageOps.exif_transpose after it.

Raw photos: some phones shot DNG. For those, the image in data/ was developed with LibRaw (rawpy, camera white balance, full resolution, JPEG quality 95). The originals are in raw/, linked by raw_file. Colours of developed DNGs differ from the phones' own JPEG processing.

Point annotations

A subset of photos carries one point per object, placed by hand in CVAT. Coordinates are pixels of the upright image, i.e. after the EXIF orientation is applied (what datasets returns), with the origin at the top left. The points are a second, independent annotation: len(kernel_points) can differ from count by a few kernels, mostly on dense sheets where a kernel was missed or lies at the edge of the frame. count stays the label.

Known limitations

Only the species in the table above so far.
Some sessions contain pollution (pollution ≠ none). It is visible in the photos and deliberately not part of the count.

License

CC BY-NC 4.0: free to use and adapt for non-commercial purposes with attribution. For commercial use, contact the Smart Farming Lab (Universität Leipzig).

Citation

No publication yet.

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