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.
OpenCAPY
OpenCAPY is a capybara semantic segmentation dataset.
Dataset Summary
OpenCAPY provides RGB images and single-channel PNG segmentation masks for capybara segmentation. Mask pixel values are class indices, not visualization values.
- Task: semantic segmentation
- Images: RGB image files under
images/ - Masks: single-channel PNG files under
labels/ - Label value
0: background - Label value
1: capybara - Training data includes real and synthetic data
- Validation and test data are real data
Dataset Structure
OpenCAPY/
├── README.md
├── RELEASE_MANIFEST.txt
├── RELEASE_REPORT.md
├── dataset.json
├── images/
├── labels/
├── metadata/
└── splits/
Each image, label, and metadata file uses the same relative stem. For example:
images/train_real/night/img_xxx.jpg
labels/train_real/night/img_xxx.png
metadata/train_real/night/img_xxx.json
Split files contain extensionless image ids, for example:
train_real/night/img_xxx
The dataset.json file lists samples with relative paths for images, labels, and metadata.
Dataset Splits
train_synthetic_qc_cleaned 23626
train_real 6494
val_real 390
test_real 382
----------------------------------
total 30892
Labels
Masks are single-channel PNG files with class-index values:
0 background
1 capybara
Do not scale training labels to 0/255. Visualization masks and preview overlays should be generated outside the canonical dataset files.
Data Sources
The release contains both real and synthetic capybara image subsets. The dataset metadata and split names distinguish real subsets from the train_synthetic_qc_cleaned synthetic training subset.
Confirmed subset names in this release:
train_realtrain_synthetic_qc_cleanedval_realtest_real
Annotation / Processing
The dataset contains segmentation masks stored as canonical 0/1 class-index PNG files. Repository metadata indicates that automated processing used Qwen grounding and SAM3 segmentation for real subsets, and that synthetic training data passed a QC-cleaned pipeline before inclusion.
This release packaging did not rerun annotation, regenerate masks, change split membership, or perform a new data-quality audit. Sample identity and split identity were preserved from the frozen source dataset.
Intended Uses
- Capybara semantic segmentation
- Computer vision research
- Segmentation benchmarking
- Real/synthetic training experiments
- Robustness and domain-gap studies involving animal segmentation
Limitations
- The dataset is focused on capybaras and may not generalize to other animals or object categories.
- Synthetic images may have a domain gap relative to real-world imagery.
- Data sources and visual distributions may contain biases.
- Users should validate generalization for their own deployment or research setting.
License / Redistribution
License and redistribution terms are being finalized before public release.
The final dataset license and original image redistribution status must be confirmed by the publisher before making the Hugging Face repository public.
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