Datasets:

Overlaiai
/

SubArcticPolarBear

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Mask must be a pyarrow.Array of type boolean
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1625, in _prepare_split_single
                  writer.write(example, key)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 537, in write
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 495, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 609, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 624, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2270, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2140, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 283, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3697, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1634, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 636, in finalize
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 495, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 609, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 624, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2270, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2140, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 283, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3697, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              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 1412, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 988, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1486, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1643, 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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file_size int64	camera_maker string	camera_model string
37,221,299	Canon	Canon EOS R5
33,533,883	Canon	Canon EOS R5
30,658,728	Canon	Canon EOS R5
28,067,778	Canon	Canon EOS R5
27,752,025	Canon	Canon EOS R5
27,743,218	Canon	Canon EOS R5
27,315,688	Canon	Canon EOS R5
27,003,321	Canon	Canon EOS R5
26,789,435	Canon	Canon EOS R5
26,808,973	Canon	Canon EOS R5
27,161,056	Canon	Canon EOS R5
26,717,211	Canon	Canon EOS R5
26,380,434	Canon	Canon EOS R5
28,197,255	Canon	Canon EOS R5
28,335,862	Canon	Canon EOS R5
28,602,376	Canon	Canon EOS R5
27,995,231	Canon	Canon EOS R5
28,072,912	Canon	Canon EOS R5
27,868,761	Canon	Canon EOS R5
27,118,311	Canon	Canon EOS R5
26,858,184	Canon	Canon EOS R5
26,914,905	Canon	Canon EOS R5
26,843,276	Canon	Canon EOS R5
25,668,921	Canon	Canon EOS R5
28,264,365	Canon	Canon EOS R5
25,087,012	Canon	Canon EOS R5
25,926,766	Canon	Canon EOS R5
25,791,567	Canon	Canon EOS R5
25,778,811	Canon	Canon EOS R5
25,719,801	Canon	Canon EOS R5
25,730,718	Canon	Canon EOS R5
25,695,224	Canon	Canon EOS R5
25,716,712	Canon	Canon EOS R5
25,785,863	Canon	Canon EOS R5
26,145,589	Canon	Canon EOS R5
31,042,423	Canon	Canon EOS R5
26,244,938	Canon	Canon EOS R5
26,238,007	Canon	Canon EOS R5
26,303,755	Canon	Canon EOS R5
26,436,976	Canon	Canon EOS R5
21,343,815	Canon	Canon EOS R5
26,326,479	Canon	Canon EOS R5
26,095,715	Canon	Canon EOS R5
26,183,712	Canon	Canon EOS R5
26,477,346	Canon	Canon EOS R5
26,361,818	Canon	Canon EOS R5
31,916,672	Canon	Canon EOS R5
26,447,243	Canon	Canon EOS R5
26,468,570	Canon	Canon EOS R5
26,091,545	Canon	Canon EOS R5
26,109,141	Canon	Canon EOS R5
26,121,239	Canon	Canon EOS R5
26,375,039	Canon	Canon EOS R5
26,366,216	Canon	Canon EOS R5
26,418,247	Canon	Canon EOS R5
26,236,545	Canon	Canon EOS R5
26,221,728	Canon	Canon EOS R5
32,097,684	Canon	Canon EOS R5
26,335,860	Canon	Canon EOS R5
26,537,813	Canon	Canon EOS R5
26,576,320	Canon	Canon EOS R5
26,563,861	Canon	Canon EOS R5
26,500,938	Canon	Canon EOS R5
26,473,772	Canon	Canon EOS R5
26,450,815	Canon	Canon EOS R5
26,466,545	Canon	Canon EOS R5
26,448,336	Canon	Canon EOS R5
26,180,028	Canon	Canon EOS R5
34,510,809	Canon	Canon EOS R5
26,588,445	Canon	Canon EOS R5
26,484,868	Canon	Canon EOS R5
26,435,732	Canon	Canon EOS R5
26,478,845	Canon	Canon EOS R5
26,462,392	Canon	Canon EOS R5
26,899,012	Canon	Canon EOS R5
26,833,389	Canon	Canon EOS R5
20,232,146	Canon	Canon EOS R5
21,871,450	Canon	Canon EOS R5
20,581,505	Canon	Canon EOS R5
34,211,094	Canon	Canon EOS R5
21,121,605	Canon	Canon EOS R5
21,336,712	Canon	Canon EOS R5
20,645,079	Canon	Canon EOS R5
20,445,262	Canon	Canon EOS R5
21,359,603	Canon	Canon EOS R5
25,980,735	Canon	Canon EOS R5
26,834,635	Canon	Canon EOS R5
26,497,935	Canon	Canon EOS R5
26,809,294	Canon	Canon EOS R5
27,046,518	Canon	Canon EOS R5
34,368,457	Canon	Canon EOS R5
27,625,177	Canon	Canon EOS R5
27,628,803	Canon	Canon EOS R5
27,723,877	Canon	Canon EOS R5
27,290,093	Canon	Canon EOS R5
27,471,623	Canon	Canon EOS R5
27,554,304	Canon	Canon EOS R5
27,684,117	Canon	Canon EOS R5
27,481,769	Canon	Canon EOS R5
27,849,849	Canon	Canon EOS R5

End of preview.

Sub Arctic Polar Bear

"Sub Arctic Polar Bear" is a fine tuning text-to-image dataset consisting of dynamic photographs captured in 30MP+ resolution using Canon R5 and a Canon 600mm RF lens.

Key Features

📷 Detail: Every photograph is captured at the proper exposure, full sharpness, delivering rich detail ideal for fine tuning polar bear datasets.
🎞️ Variance: Includes close-up details, behavioural, low perspective ground level, all angles of the polar bears, medium and far shots in habitat, autumn and winter scenes.
⚙️ Consistency: Re-thinking training data at the point of capture by "overshooting" a subject, enabling models to learn more nuanced relationships and views across scenes.
🌅 Light: Shot during early morning, sunset light for optimal color contrast and dynamic range. Also, shot during bright overcast, maximizing visual quality for color and lighting-sensitive tasks.
🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.

Dataset Details

Total photos: 1,801
Total size: 55.01 GB
Photo types: Close-ups, medium distance, habitat, portraits, side profile, all angles, awake, walking, sleeping, eating.

Technical Details

Cameras: Canon R5
Lenses: Canon 600mm RF, Canon 70-200mm EF
Resolution: 8192x5464
Colorspace: Adobe RGB 1998
Image Size: 35MP+

Content Credentials

Each video in the "Sub Arctic Polar Bear" dataset contains C2PA Content Credentials metadata and can be used with the Content Credentials Space by Truepic to begin testing model provenance.

What is Overlai.ai?

Overlai.ai specializes in 8K+ fine tuning datasets with the goal of improving photo & video models. Contact us for the complete dataset list or access to this dataset in full resolution.

Contact

[email protected]

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