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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
                  for key, record in generator:
                                     ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                                     ^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
                  for x in self.generator(*self.args):
                           ~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
                  with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
                       ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
                  file_obj = fs.open(paths[0], mode)
                File "<string>", line 3, in open
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
                  return self._mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
                  return self._execute_mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
                  result = effect(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
                  tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
                                                               ~~~^^^^^^^^
              TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
              
              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/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6801
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6813
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6841
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6807
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6847
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6826
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6815
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6827
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6845
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6819
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6834
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6838
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6816
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6806
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6832
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6817
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6837
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6810
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6830
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6822
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6839
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6842
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6808
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6848
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6846
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6811
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6844
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6804
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6820
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6833
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6821
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6825
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6835
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6824
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6836
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6831
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6829
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6828
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6843
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6805
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6812
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6818
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6802
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6803
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6823
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6840
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6849
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6814
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6809
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n04179913/test_6800
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6148
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6135
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6107
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6144
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6143
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6140
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6130
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6139
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6127
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6113
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6134
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6101
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6119
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6129
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6149
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6108
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6123
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6147
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6124
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6138
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6100
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6133
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6146
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6114
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6103
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6116
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6142
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6141
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6120
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6112
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6109
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6125
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6118
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6131
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6110
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6128
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6106
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6136
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6145
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6122
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6115
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6105
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6126
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6104
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6117
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6137
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6132
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6121
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6102
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
Tiny-Imagenet-C/gaussian_noise/2/n03970156/test_6111
hf://datasets/ifm23/TinyImageNet-C@78800121946a9da359f1229770d4070ad3857527/tiny-imagenet-c.tar
End of preview.

TinyImageNet-C data set offered on Zendo (https://zenodo.org/records/2536630) only consists of 182/200 test classes. As a result, this version of TinyImageNet-C data contains all 200 classes using the same corruptions provided in the oprginal paper.

The 15 corruptions in the dataset are as follows:

  • shot_noise
  • brightness
  • pixelate
  • glass_blur
  • motion_blur
  • impulse_noise
  • frost
  • jpeg_compression
  • contrast
  • defocus_blur
  • elastic_transform
  • snow
  • fog
  • gaussian_noise
  • zoom_blur

There are 5 severity categories (1-5) for each corruption type. The lables match the orginal TinyImageNet data set which can be found on hugging face at: zh-plus/tiny-imagenet

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