Datasets:
Dataset Viewer
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: CastError
Message: Couldn't cast
version: string
flags: struct<>
shapes: list<item: struct<label: string, points: list<item: list<item: double>>, group_id: null, shape_type: (... 26 chars omitted)
child 0, item: struct<label: string, points: list<item: list<item: double>>, group_id: null, shape_type: string, fl (... 14 chars omitted)
child 0, label: string
child 1, points: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, group_id: null
child 3, shape_type: string
child 4, flags: struct<>
imagePath: string
imageData: string
imageHeight: int64
imageWidth: int64
info: struct<description: string, date_created: string>
child 0, description: string
child 1, date_created: string
images: list<item: struct<id: int64, file_name: string, width: int64, height: int64>>
child 0, item: struct<id: int64, file_name: string, width: int64, height: int64>
child 0, id: int64
child 1, file_name: string
child 2, width: int64
child 3, height: int64
categories: list<item: struct<id: int64, name: string, supercategory: string>>
child 0, item: struct<id: int64, name: string, supercategory: string>
child 0, id: int64
child 1, name: string
child 2, supercategory: string
annotations: list<item: struct<id: int64, image_id: int64, category_id: int64, bbox: list<item: double>, area: do (... 22 chars omitted)
child 0, item: struct<id: int64, image_id: int64, category_id: int64, bbox: list<item: double>, area: double, iscro (... 10 chars omitted)
child 0, id: int64
child 1, image_id: int64
child 2, category_id: int64
child 3, bbox: list<item: double>
child 0, item: double
child 4, area: double
child 5, iscrowd: int64
licenses: list<item: null>
child 0, item: null
to
{'info': {'description': Value('string'), 'date_created': Value('string')}, 'licenses': List(Value('null')), 'categories': List({'id': Value('int64'), 'name': Value('string'), 'supercategory': Value('string')}), 'images': List({'id': Value('int64'), 'file_name': Value('string'), 'width': Value('int64'), 'height': Value('int64')}), 'annotations': List({'id': Value('int64'), 'image_id': Value('int64'), 'category_id': Value('int64'), 'bbox': List(Value('float64')), 'area': Value('float64'), 'iscrowd': Value('int64')})}
because column names don't match
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/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
version: string
flags: struct<>
shapes: list<item: struct<label: string, points: list<item: list<item: double>>, group_id: null, shape_type: (... 26 chars omitted)
child 0, item: struct<label: string, points: list<item: list<item: double>>, group_id: null, shape_type: string, fl (... 14 chars omitted)
child 0, label: string
child 1, points: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, group_id: null
child 3, shape_type: string
child 4, flags: struct<>
imagePath: string
imageData: string
imageHeight: int64
imageWidth: int64
info: struct<description: string, date_created: string>
child 0, description: string
child 1, date_created: string
images: list<item: struct<id: int64, file_name: string, width: int64, height: int64>>
child 0, item: struct<id: int64, file_name: string, width: int64, height: int64>
child 0, id: int64
child 1, file_name: string
child 2, width: int64
child 3, height: int64
categories: list<item: struct<id: int64, name: string, supercategory: string>>
child 0, item: struct<id: int64, name: string, supercategory: string>
child 0, id: int64
child 1, name: string
child 2, supercategory: string
annotations: list<item: struct<id: int64, image_id: int64, category_id: int64, bbox: list<item: double>, area: do (... 22 chars omitted)
child 0, item: struct<id: int64, image_id: int64, category_id: int64, bbox: list<item: double>, area: double, iscro (... 10 chars omitted)
child 0, id: int64
child 1, image_id: int64
child 2, category_id: int64
child 3, bbox: list<item: double>
child 0, item: double
child 4, area: double
child 5, iscrowd: int64
licenses: list<item: null>
child 0, item: null
to
{'info': {'description': Value('string'), 'date_created': Value('string')}, 'licenses': List(Value('null')), 'categories': List({'id': Value('int64'), 'name': Value('string'), 'supercategory': Value('string')}), 'images': List({'id': Value('int64'), 'file_name': Value('string'), 'width': Value('int64'), 'height': Value('int64')}), 'annotations': List({'id': Value('int64'), 'image_id': Value('int64'), 'category_id': Value('int64'), 'bbox': List(Value('float64')), 'area': Value('float64'), 'iscrowd': Value('int64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ASABE Robotics Plant Detection Dataset (2026)
This dataset contains plant images captured for the ASABE Robotics Competition.
Dataset Summary
- Repository:
heesup/2026-asabe-robotics-corn-detection - Total Clean Images: 213 images
- Annotated Images: 190 images (107 bounding box annotations)
- Categories:
green_plant(ID 1 in COCO, ID 0 in YOLO)yellow_plant(ID 2 in COCO, ID 1 in YOLO)
Privacy & Data Filtering Notice
Before publication, all images in the raw dataset (651 initial frames) were scanned using a pre-trained YOLOv8 object detector (yolov8n.pt) to detect human presence (hands, arms, clothing, body parts, or people in frame).
- Raw Images Scanned: 651
- Human Detections Filtered Out: 437 images
- Corrupted Files Removed: 1 image
- Clean Dataset Size: 213 images
Directory Structure
annotations.json: Standard COCO format bounding box annotations.images/: PNG images along with per-image LabelMe JSON annotation files.data.yaml: Ultralytics YOLO model configuration file.
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