Datasets:

Pacific-i64
/

complexity-atlas-image-edits

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 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              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 71, 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.

Complexity Atlas Image Edits

Complexity Atlas Image Edits is an aligned instruction-guided image editing dataset derived from the normalized public-domain Complexity Atlas image bank. It contains 336,245 explicit source/instruction/target triplets at 256 x 256.

Dataset structure

Each WebDataset record contains:

<edit_id>.source.webp
<edit_id>.target.webp
<edit_id>.txt
<edit_id>.json

source.webp is a deterministic degraded image;
target.webp is the unchanged normalized public-domain artwork;
txt is an original imperative restoration instruction;
json records transformation parameters, hashes, provenance, source URL, upstream revision, source license, and split identity.

Scale

Property	Value
Editing pairs	336,245
Train	329,510
Validation	3,428
Test	3,307
WebDataset shards	136
Stored bytes	11,302,287,360
Edit families	13
Exact instruction variants	126
Maximum exact instruction share	1.56%

Editing families

The first release covers color restoration, deblurring, underexposure and overexposure correction, contrast and saturation restoration, detail recovery, compression restoration, warm and cool cast correction, orientation correction, surface-damage restoration, and missing-region restoration.

These are verifiable restoration operations. The release does not claim to contain semantic object insertion, removal, identity changes, or unconstrained creative edits.

Construction and split integrity

One deterministic edit is selected per source artwork. The original target's split is preserved, so an artwork cannot cross train, validation, and test. The complete audit reports:

zero source-identity leakage;
zero rejected records;
complete source/instruction/target/metadata membership;
a non-trivial pixel change for every source;
unique edit identifiers;
exact instruction repetition below 5%.

See audit_report.json, build_manifest.json, and files_manifest.json for machine-readable evidence.

Provenance and licensing

The normalized target images originate from Mitsua/art-museums-pd-440k at pinned revision fba945da78b36262eb9272067197cc28d06cffbf. Every retained record is marked CC0 and retains its source URL and upstream metadata.

Generated source images are deterministic mechanical transformations of those targets. This repository does not impose a new blanket copyright restriction on the public-domain image assets. The builder code is available separately under Apache-2.0. Users remain responsible for reviewing the per-record provenance for their intended jurisdiction and use.

Builder

The reproducible builder and tests are available at:

https://github.com/Complexity-ML/complexity-atlas-images

Downloads last month: 19

Total file size:

11.3 GB