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Datasets:
anilbhujel
/
Gilt_tracking_dataset

Tasks:
Object Detection
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Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
                  obj = self._get_object_parser(self.data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
                  self._parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1403, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3357, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2111, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2315, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 476, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 323, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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Gilt Tracking Dataset

  • The videos are split into 5-minute lengths. The video filenames are assigned according to the recording start timestamp, pen, and camera types.
  • Virtual IDs are assigned. RFID reading data is provided with a timestamp and ID for mapping later.

🧠 Use Cases

  • Animal behavior monitoring and tracking
  • Multiview videos from two sides of the pen
  • Precision livestock farming
  • Continuous tracking animal for half an hour of video

License

The author has granted permission to download, use, and redistribute this dataset only for research purposes.

Citation

Please cite as Bhujel A. et al. (2025). A computer vision dataset for Gilt daily posture activity monitoring and tracking.

Contact

For questions or collaborations, feel free to reach out at [email protected]

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