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Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 64, in _split_generators
                  with h5py.File(first_file, "r") as h5:
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/h5py/_hl/files.py", line 564, in __init__
                  fid = make_fid(name, mode, userblock_size, fapl, fcpl, swmr=swmr)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/h5py/_hl/files.py", line 238, in make_fid
                  fid = h5f.open(name, flags, fapl=fapl)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "h5py/_objects.pyx", line 56, in h5py._objects.with_phil.wrapper
                File "h5py/_objects.pyx", line 57, in h5py._objects.with_phil.wrapper
                File "h5py/h5f.pyx", line 102, in h5py.h5f.open
              FileNotFoundError: [Errno 2] Unable to synchronously open file (unable to open file: name = 'hf://datasets/konpat/circle-behavior-sfr-4f-300k@320209e477011a81cc8775f14cbd6d2b5ecbf526/train.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
              
              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 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, 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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circle_behavior_SFR_4frames_300k

SFR 4-frame 300k: Color Switch (infinite), Size Fixed (20px), Speed Random - color switching learning

Statistics

Training videos: 300,000
Test videos: 10,000 (shared with 30k dataset)
Total videos: 310,000
Frames per video: 4 (2 first half + 2 second half)
Resolution: 256x256
Storage format: HDF5 with MP4-encoded videos

Compact Frame Structure

Frame 1-2: First half of motion sequence
Frame 3-4: Second half of motion sequence
Switch Point: Between frames 2 and 3

Behavior Configuration (SFR Pattern)

S (Switch) Color: Random/infinite variety per video - primary learning variable
F (Fixed) Size: 20px radius - consistent across all videos
R (Random) Speed: Random velocity per video - secondary variation

Sample Visualizations

Video Sequence Examples

Here are sample 4-frame sequences showing the SFR behavior pattern (horizontal temporal progression):

Sample 1 - Color Switch Pattern:

Sample 2 - Different Color Transitions:

Sample 3 - Varied Movement Pattern:

Key Visual Features

Color Switching: Colors change unpredictably between frames
Fixed Size: All circles maintain consistent 20-pixel radius
Random Speed: Movement velocity varies between videos
Large-Scale: 300k samples provide comprehensive coverage of color patterns

Research Value

This dataset enables color switching pattern learning in compact 4-frame format, focusing on visual attribute adaptation while controlling for size and allowing speed variation.

Large-Scale Benefits

Enhanced Statistical Power: 300k samples for robust color switching analysis
Scale Comparison: Direct comparison with 30k SFR using identical test set
Consistent Evaluation: Shared test set enables precise scale effect measurement

Generated with bounds-safe CircleVideoGenerator.

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