The dataset viewer is not available for this subset.
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/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
BeeMachine Partwhole Dataset
Pixel-level anatomical part annotations for bumble bee photographs used by the BeeMachine identification service and by the IAAI-27 evaluation of anatomy-guided learning strategies.
Summary
| Property | Value |
|---|---|
| Images | 7,716 |
| Species | 160 |
| Part classes | background, abdomen, head, thorax |
| Mask format | integer PNG (*_m.png), same stem as the RGB image |
| Labels | species_labels.csv (images, species) |
Directory layout
Files in this repository:
README.md
species_labels.csv # image filename → species
annotations.coco.json # COCO-format segmentation export
images.tar.gz # RGB photographs (flat directory `images/`)
masks.tar.gz # integer part masks (flat directory `masks/`)
After download, unpack next to the CSV:
tar -xzf images.tar.gz
tar -xzf masks.tar.gz
This yields:
images/ # RGB photographs (flat)
masks/ # integer part masks, one {stem}_m.png per image
species_labels.csv
annotations.coco.json
Mask class IDs:
| ID | Label |
|---|---|
| 0 | background |
| 1 | abdomen |
| 2 | head |
| 3 | thorax |
Intended use
Train and evaluate part segmenters that supply anatomical masks to species classifiers. In the associated paper, this corpus is the Stage A segmentation set only; species classification is trained on a separate, larger originals-only BeeMachine classification corpus.
Note: The full BeeMachine classification dataset is very large and is not hosted here; it will be available upon request.
Splits
The paper uses a frozen species-stratified 75% / 15% / 10% train / validation / test split (seed 42). Those fold filenames are distributed with the supplementary code under outputs/beemachine/frozen_splits/. Train-only six-fold geometric augmentation (original, flips, 90/180/270° rotations) is generated from the frozen training fold and is not shipped here.
Source photographs
Images were collected from citizen-science and museum archives used by BeeMachine (including Bumble Bee Watch, iNaturalist, and BugGuide). Part masks were annotated for head, thorax, and abdomen. Copyright in the underlying photographs remains with the original rights holders; redistribution here is for non-commercial research.
Citation
If you use this dataset, please cite the BeeMachine paper and the IAAI-27 evaluation that releases these part annotations:
@article{spiesman2021beemachine,
author = {Spiesman, Brian J. and Gratton, Claudio and Hatfield, Richard G. and Hsu, William H. and Jepsen, Sarina and McCornack, Brian and Patel, Krishna and Wang, Guanjie},
title = {Assessing the Potential for Deep Learning and Computer Vision to Identify Bumble Bee Species from Images},
journal = {Scientific Reports},
volume = {11},
pages = {7580},
year = {2021},
doi = {10.1038/s41598-021-87210-1}
}
License
MIT for the annotation files and packaging in this repository. Photograph copyrights remain with their original owners.
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