Datasets:

davidesapienza
/

UnderWaterNet

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.

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UnderWaterNet Dataset

Official repository for the UnderWaterNet dataset, a comprehensive collection of underwater and surface images for computer vision tasks including 2D object detection and 6D pose estimation.

Paper

Model-Based Underwater 6D Pose Estimation From RGB
Published in IEEE Xplore

Official Website

https://sapienzadavide.github.io/uwpose.github.io/index.html

Dataset Structure

Real Underwater & Surface Images (UWds)

Compressed archives of real-world images across different environmental conditions:

UW6d/asphalt.tar.gz (4.6GB) - Asphalt surface images
UW6d/drydirt.tar.gz (3.4GB) - Dry dirt surface images
UW6d/dryshadow.tar.gz (2.5GB) - Shadowed dry surface images
UW6d/drywhite.tar.gz (3.8GB) - White dry surface images
UW6d/grass.tar.gz (4.5GB) - Grass surface images
UW6d/uwLED.tar.gz (530MB) - Underwater LED-lit images
UW6d/uwshadow.tar.gz (1.9GB) - Underwater shadowed images
UW6d/w_p_occluded.tar.gz (956MB) - Water with partial occlusion

3D Object Models (UW6d)

UW6d/objects_ply/ - PLY mesh files for 6D pose estimation tasks

2D Object Detection Dataset (UW2d)

Real-world images for 2D object detection:

UW2d/cube.tar.gz - Cube object images
UW2d/cup.tar.gz - Cup object images
UW2d/hotstab.tar.gz - Hotstab object images
UW2d/jar.tar.gz - Jar object images
UW2d/multiple_objects.tar.gz - Multi-object scenes

Synthetic Datasets

Unity-generated synthetic data for training:

synthetic_2d.tar.gz - Synthetic dataset for 2D object detection (no background)
synthetic_6d.tar.gz - Synthetic dataset for 6D pose estimation (sandy background)

Tasks Supported

6D pose estimation
2D object detection

Citation

If you use this dataset in your research, please cite:

@ARTICLE{10265120,
  author={Sapienza, Davide and Govi, Elena and Aldhaheri, Sara and Bertogna, Marko and Roura, Eloy and Pairet, Èric and Verucchi, Micaela and Ardón, Paola},
  journal={IEEE Robotics and Automation Letters}, 
  title={Model-Based Underwater 6D Pose Estimation From RGB}, 
  year={2023},
  volume={8},
  number={11},
  pages={7535-7542},
  keywords={Pose estimation;Three-dimensional displays;Sensors;Solid modeling;Task analysis;Cameras;Robot sensing systems;Computer vision for manipulation;dataset;manipulation;pose estimation;underwater},
  doi={10.1109/LRA.2023.3320028}
  }

License

openrail

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