The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
v_bYUmtLBL7W4: struct<duration: double, subset: string, resolution: string, url: string, annotations: list<item: struct<segment: list<item: double>, label: string>>>
v_hDPLy21Yyuk: struct<duration: double, subset: string, resolution: string, url: string, annotations: list<item: struct<segment: list<item: double>, label: string>>>
vs
results: struct<bYUmtLBL7W4: list<item: struct<label: string, score: double, segment: list<item: double>>>, hDPLy21Yyuk: list<item: struct<label: string, score: double, segment: list<item: double>>>>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
for key, example in iterator:
^^^^^^^^
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 2082, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 604, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
v_bYUmtLBL7W4: struct<duration: double, subset: string, resolution: string, url: string, annotations: list<item: struct<segment: list<item: double>, label: string>>>
v_hDPLy21Yyuk: struct<duration: double, subset: string, resolution: string, url: string, annotations: list<item: struct<segment: list<item: double>, label: string>>>
vs
results: struct<bYUmtLBL7W4: list<item: struct<label: string, score: double, segment: list<item: double>>>, hDPLy21Yyuk: list<item: struct<label: string, score: double, segment: list<item: double>>>>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
turn-taking-detection-dataset
scripts/notebooks for turn taking detection dataset
Frame Feature extraction
Currently, extracting feature with mmaciton: current method needs to be fix. It is difference from the OAD convention.
current method: please refer this
Only taking the central frame among non-overlapping video snippets. In OAD task, all the frames in from snippets are converted into the mean of features from them.
To-dos (legacy, now maintain @ notion)
Data Preprocessing
- Split ego4d videos
- Extract RGB rawframes (ongoing)
We do not use clip-level feature, we need to extract all rawframes from each video, for detail, see LSTR repo below - (secondary) Extract flow rawframes (getting flow rawframes is tricky because of installing denseflow)
- Convert transcript timestamp into frame-level annotation
Still do not know how to convert frame-level annoation of a video into frame-level annotation of a feature
Feature extraction (this would be help for extracting features)
- Extract RGB features with TSN, following instructions
- Extract audio features
- (secondary) Extract flow features
Problems
Current extracted feature does not fit to the testra feature (Ours temporal dimension T in [T x 2048] are rounded, while testra feature are always rounded down)
Docs
Following LSTR, planning to use mmaction 0.x instead of 1.x, be careful with the mmaction version
LSTR also followed mmaction2 activitynet pipeline, and convert features into .npy
Many details are in LSTR and mmaction issues
mmaction dataset preparation doc
mmaction feature extraction (based on 0.x, deprecated)
mmaction preparing AcitivityNet
mmaction preparing THUMOS14
mmaction TSN
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