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Datasets:
us-segmentator
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us-segmentation-dataset

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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label Breast
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2092, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
                  return feature.cast_storage(array)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1169, in cast_storage
                  [self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1098, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label Breast

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Check out the documentation for more information.

US Segmentation Dataset

The dataset is a collection of openly available ultrasound datasets.

The individual datasets are stored as zip files in the zip directory. The utility for extraction all of them will be provided later.

Instructions for uploading new datasets

  1. Download the dataset from the source
  2. Convert the dataset into a zip format if required
  3. Upload the zip file corresponding anatomy folder in the zip directory using the command: hf upload [repo_id] [local_path] [path_in_repo] --repo-type dataset --commit-message "add-dataset: <dataset_name>"

eg: hf upload us-segmentator/us-segmentation-dataset local_path_to_zip_file.zip zips/<anatomy_name>/<zip_file_name>.zip --repo-type dataset --commit-message "add-dataset: <dataset_name>"

To Download the dataset, use the following command:

  1. Make sure you have git lfs installed
  2. Run the following command:
git lfs install
  1. Run the following command:
hf download us-segmentator/us-segmentation-dataset --repo-type dataset --local-dir local_path_to_download_directory
  1. The dataset will be downloaded to the local directory.

Note: The dataset can be downloaded without the --local-dir argument, but it will be downloaded the huggingface cache directory.
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