Datasets:

VR-VLA
/

VR-egodex-depth-cache-full

Tasks:

Robotics

Tags:

egocentric

egodex

depth

License:

Dataset card Data Studio Files Files and versions

xet

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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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png image	__key__ string	__url__ string
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0041	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0046	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0140	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0203	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0180	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0071	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0142	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0189	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0072	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0106	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0199	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0102	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0098	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0018	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0152	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0000	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0125	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0019	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0116	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0127	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0023	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0162	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0079	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0131	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0066	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0022	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0115	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0158	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0002	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
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	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0068	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0056	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0123	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0166	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0124	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0113	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0025	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0086	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0030	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0164	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0031	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0091	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0138	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0201	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0111	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0126	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0021	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0174	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0078	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0067	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0136	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0001	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0146	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0077	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0157	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0193	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0196	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0095	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0064	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0170	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0182	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0148	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0082	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0005	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0047	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0147	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0194	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar
	set2/train/add_remove_lid_v2.1/depth/episode1113/frame0160	hf://datasets/VR-VLA/VR-egodex-depth-cache-full@766e15ce855372e307bb78cf5d8a69ff2bb55df3/set2/train/add_remove_lid_v2.1.tar

End of preview.

EgoDex Depth Cache

Per-frame metric scene depth (Depth-Anything-V2 Metric Indoor Large, 20 m ceiling) for the episodes inpainted in VR-VLA/VR-egodex-inpaint-cache-full. Depth is the second input Eq. 4 compositing needs beside the inpainted plate; together they let scripts/egodex/build_task.py retarget an episode onto all 16 embodiments.

Pairing with the inpaint cache

Archive names are IDENTICAL to the inpaint repo, one tar per <split>/<task>:

inpaint  <split>/<task>.tar -> <split>/<task>/inpaint_diffueraser/episode<N>/frame%04d.png
depth    <split>/<task>.tar -> <split>/<task>/depth/episode<N>/frame%04d.png

Episode numbers match the source dataset's episode_%06d.mp4. <split> is part of the key: most task names exist in both train/ and test/ with overlapping episode numbers.

Each archive expands to:

<split>/<task>/depth/episode<N>/frame%04d.png   one per source frame, 0-based, 30 fps
<split>/<task>/depth/episode<N>/done.json       {num_frames, format, created}

Archives are uncompressed tar (PNG payload). train/add_remove_lid_v2.1.tar (308 GB, 1,158 episodes) exceeds the Hub's 50 GB file limit and is stored as seven .tar.NN.part files; cat train/add_remove_lid_v2.1.tar.*.part > train/add_remove_lid_v2.1.tar before extracting.

Format

16-bit grayscale PNG, native 1920x1080, fixed scale: metres = value / 65535 * 20.0 (Depth-Anything-V2's metric-indoor ceiling; values beyond 20 m clamp). Resolution 0.31 mm, ~1.0 MB/frame, 5x smaller than float16 .npy. Fixed scale, not per-frame min/max, so no sidecar is needed and frames are directly comparable.

import numpy as np
from PIL import Image
depth_m = np.asarray(Image.open('frame0000.png'), dtype=np.float32) / 65535.0 * 20.0  # (1080, 1920)

In the retarget repo: png_to_depth() in src/retarget_egocentric_data/retarget/visual/stage3_scene_depth.py; load_visual_alignment_cache() accepts .png or .npy transparently.

Batches

Batch membership is per EPISODE. A task tar on the inpaint repo can hold episodes of several batches; batches.json in this repo lists, per <split>/<task>, which episode ids belong to which batch. Each batch is retargeted on ONE server; a server builds its manifest with scripts/egodex/manifest.py --batches batches.json --batch N and only that batch's episodes are staged, so no episode is ever retargeted twice.

batch	content	episodes	frames	archives	depth here
1	original server's local cache (`EgodexRetarget`)	659	282,240	—	after batch-1 campaign finishes
2	every other task, remaining episodes	1,050	405,111	232	yes
3	`train/add_remove_lid_v2.1` only (43% of remaining frames)	1,158	311,580	1 (308 GB, 7 parts)	yes

Batch 3 is one task on purpose: it alone is as large as the rest of the corpus, so the server that takes it downloads exactly two archives (inpaint + depth) and nothing else. Episodes under 23 frames have masks but no inpainted frames in the inpaint repo and are in no batch (masks_only in batches.json).

Workflow on a new server (batch N = 2 or 3)

Fetch batches.json first — it is the source of truth for what is left to do: hf download VR-VLA/VR-egodex-depth-cache-full batches.json README.md --repo-type dataset --local-dir <depth>
batches.json['batchN_tars'] lists the archives to fetch, by identical name from BOTH repos (batch 2: 232 tars; batch 3: 1 tar, stored as .tar.NN.part pieces in both repos). Inpaint tars also contain other batches' episodes — that is fine, the next step filters them.
python scripts/egodex/manifest.py --cache-root <inpaint> --depth-cache-root <depth> --batches <depth>/batches.json --batch N --out <OUT_ROOT>/_manifest/episode_manifest.json keeps only batchN episode ids per task.
scripts/egodex/launch_batch2.sh — same launcher for batch 2 and 3, no prototype videos (see scripts/egodex/README.md, "Batches").

Per-task episode ids

task	batch 1	batch 2	batch 3
`test/add_remove_lid_v2.1`	0, 1, 2	3, 4, 5	-
`test/arrange_topple_dominoes_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_disassemble_furniture_bench_chair_v2.1`	0, 1, 2	3, 4, 5, 6	-
`test/assemble_disassemble_furniture_bench_desk_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_disassemble_furniture_bench_drawer_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_disassemble_furniture_bench_lamp_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_disassemble_furniture_bench_square_table_v2.1`	0, 1, 2	3, 4, 5, 6	-
`test/assemble_disassemble_furniture_bench_stool_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_disassemble_legos_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_disassemble_soft_legos_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_disassemble_structures_v2.1`	0, 1, 2	3, 4, 5, 6	-
`test/assemble_disassemble_tiles_v2.1`	0, 1, 2	3, 4, 5	-
`test/assemble_jenga_v2.1`	0, 1, 2	3, 4, 5	-
`test/basic_fold_v2.1`	0, 1, 2	3, 4, 5	-
`test/basic_pick_place_v2.1`	0, 1, 2	3, 4, 5, 6	-
`test/boil_serve_egg_v2.1`	0, 1, 2	3, 4	-
`test/braid_unbraid_v2.1`	0, 1, 2	3, 4, 5	-
`test/build_unstack_lego_v2.1`	0, 1, 2	3, 4, 5	-
`test/charge_uncharge_airpods_v2.1`	0, 1, 2	3, 4, 5	-
`test/charge_uncharge_device_v2.1`	0, 1, 2	3, 4, 5	-
`test/clean_cups_v2.1`	0, 1, 2	3, 4, 5	-
`test/clean_surface_v2.1`	0, 1, 2	3, 4, 5	-
`test/clean_tableware_v2.1`	0, 1, 2	3, 4, 5	-
`test/clip_unclip_papers_v2.1`	0, 1, 2	3, 4	-
`test/color_v2.1`	0, 1, 2	3, 4, 5	-
`test/crumple_flatten_paper_v2.1`	0, 1, 2	3, 4	-
`test/deal_gather_cards_v2.1`	0, 1, 2	3, 4, 5	-
`test/declutter_desk_v2.1`	0, 1, 2	3, 4, 5	-
`test/dry_hands_v2.1`	0, 1, 2	3, 4, 5	-
`test/fidget_magnetic_spinner_rings_v2.1`	0, 1, 2	3, 4, 5	-
`test/flip_coin_v2.1`	0, 1	-	-
`test/flip_pages_v2.1`	0, 1, 2	3, 4, 5	-
`test/fold_stack_unstack_unfold_cloths_v2.1`	0, 1, 2	3, 4, 5	-
`test/fold_unfold_paper_basic_v2.1`	0, 1, 2	3, 4, 5	-
`test/fold_unfold_paper_origami_v2.1`	0, 1, 2	3, 4, 5	-
`test/fry_bread_v2.1`	0	-	-
`test/fry_egg_v2.1`	0	-	-
`test/gather_roll_dice_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_dump_blocks_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_airpods_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_bagging_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_bookshelf_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_cups_from_rack_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_drawer_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_furniture_bench_cabinet_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_furniture_bench_round_table_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_plug_socket_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_shirt_in_tube_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_tennis_ball_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_usb_v2.1`	0, 1, 2	3, 4, 5	-
`test/insert_remove_utensils_v2.1`	0, 1, 2	3, 4, 5	-
`test/knead_slime_v2.1`	0, 1, 2	3, 4, 5	-
`test/load_dispense_ice_v2.1`	0, 1, 2	3, 4, 5	-
`test/lock_unlock_key_v2.1`	0, 1, 2	3, 4, 5	-
`test/make_sandwich_v2.1`	0, 1, 2	3, 4, 5	-
`test/measure_objects_v2.1`	0, 1, 2	3, 4, 5	-
`test/open_close_insert_remove_box_v2.1`	0, 1, 2	3, 4, 5	-
`test/open_close_insert_remove_case_v2.1`	0, 1, 2	3, 4, 5	-
`test/open_close_insert_remove_tupperware_v2.1`	0, 1, 2	3, 4, 5	-
`test/paint_clean_brush_v2.1`	0, 1, 2	3, 4, 5	-
`test/peel_place_sticker_v2.1`	0, 1, 2	3, 4, 5	-
`test/pick_place_food_v2.1`	0, 1, 2	3, 4, 5	-
`test/pick_up_and_put_down_case_or_bag_v2.1`	0, 1, 2	3, 4, 5	-
`test/play_mancala_v2.1`	0, 1, 2	3, 4, 5	-
`test/play_piano_v2.1`	0, 1, 2	3, 4, 5	-
`test/play_reset_connect_four_v2.1`	0, 1, 2	3, 4, 5	-
`test/point_and_click_remote_v2.1`	0, 1, 2	3, 4, 5	-
`test/pour_v2.1`	0, 1, 2	3, 4, 5	-
`test/push_pop_toy_v2.1`	0, 1, 2	3, 4, 5	-
`test/put_away_set_up_board_game_v2.1`	0, 1, 2	3, 4, 5	-
`test/put_in_take_out_glasses_v2.1`	0, 1, 2	3, 4, 5	-
`test/put_toothpaste_on_toothbrush_v2.1`	0, 1, 2	3, 4, 5	-
`test/rake_smooth_zen_garden_v2.1`	0, 1, 2	3, 4, 5	-
`test/roll_ball_v2.1`	0, 1, 2	3, 4, 5	-
`test/scoop_dump_ice_v2.1`	0, 1, 2	3, 4, 5	-
`test/screw_unscrew_allen_fixture_v2.1`	0, 1, 2	3, 4, 5	-
`test/screw_unscrew_bottle_cap_v2.1`	0, 1, 2	3, 4, 5	-
`test/screw_unscrew_fingers_fixture_v2.1`	0, 1, 2	3, 4, 5	-
`test/set_up_clean_up_chessboard_v2.1`	0, 1, 2	-	-
`test/setup_cleanup_table_v2.1`	0, 1, 2	3, 4, 5	-
`test/sleeve_unsleeve_cards_v2.1`	0, 1, 2	3	-
`test/slot_batteries_v2.1`	0, 1, 2	3, 4, 5	-
`test/sort_beads_v2.1`	0, 1, 2	3, 4, 5	-
`test/stack_remove_jenga_v2.1`	0, 1, 2	3, 4, 5	-
`test/stack_unstack_bowls_v2.1`	0, 1, 2	3, 4, 5	-
`test/stack_unstack_cups_v2.1`	0, 1, 2	3, 4, 5	-
`test/stack_unstack_plates_v2.1`	0, 1, 2	3, 4, 5	-
`test/stack_unstack_tupperware_v2.1`	0, 1, 2	3, 4, 5	-
`test/stack_v2.1`	0, 1, 2	3, 4, 5	-
`test/staple_paper_v2.1`	0, 1, 2	3, 4, 5	-
`test/stock_unstock_fridge_v2.1`	0, 1, 2	3, 4, 5	-
`test/sweep_dustpan_v2.1`	0, 1, 2	3, 4, 5	-
`test/thread_unthread_bead_necklace_v2.1`	0, 1, 2	3, 4, 5	-
`test/throw_and_catch_ball_v2.1`	0, 1, 2	3, 4, 5	-
`test/throw_collect_objects_v2.1`	0, 1, 2	3, 4, 5	-
`test/tie_and_untie_shoelace_v2.1`	0, 1, 2	3, 4, 5	-
`test/tie_untie_rubberband_v2.1`	0, 1, 2	3, 4, 5	-
`test/type_keyboard_v2.1`	0, 1, 2	3, 4, 5	-
`test/use_chopsticks_v2.1`	0, 1, 2	3, 4, 5	-
`test/use_rubiks_cube_v2.1`	0, 1, 2	3, 4, 5	-
`test/vertical_pick_place_v2.1`	0, 1, 2	3, 4, 5	-
`test/wash_fruit_v2.1`	0	-	-
`test/wash_kitchen_dishes_v2.1`	0	-	-
`test/wash_put_away_dishes_v2.1`	0, 1	-	-
`test/wipe_kitchen_surfaces_v2.1`	0, 1, 2	3, 4, 5	-
`test/wipe_screen_v2.1`	0, 1, 2	3, 4, 5	-
`test/wrap_unwrap_food_v2.1`	0, 1, 2	3, 4, 5	-
`test/wrap_v2.1`	0, 1, 2	3, 4, 5	-
`test/write_v2.1`	0, 1, 2	3, 4, 5	-
`test/zip_unzip_bag_v2.1`	0, 1, 2	3, 4, 5	-
`test/zip_unzip_case_v2.1`	0, 1, 2	3, 4, 5	-
`train/add_remove_lid_v2.1`	-	-	1158 episodes, see batches.json
`train/arrange_topple_dominoes_v2.1`	0, 1, 2	3, 4, 5	-
`train/assemble_disassemble_furniture_bench_chair_v2.1`	0, 1, 2	3, 4, 5, 469, 471, 473, 475, 477, 479	-
`train/assemble_disassemble_furniture_bench_desk_v2.1`	0, 1, 2	3, 4, 5	-
`train/assemble_disassemble_furniture_bench_drawer_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478, 480	-
`train/assemble_disassemble_furniture_bench_lamp_v2.1`	0, 1, 2	3, 4, 5	-
`train/assemble_disassemble_furniture_bench_square_table_v2.1`	0, 1, 2	3, 4, 5, 469, 471, 473, 475, 477, 479, 481	-
`train/assemble_disassemble_furniture_bench_stool_v2.1`	0, 1, 2	3, 4, 5	-
`train/assemble_disassemble_legos_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478	-
`train/assemble_disassemble_soft_legos_v2.1`	0, 1, 2	3, 4, 5	-
`train/assemble_disassemble_structures_v2.1`	0, 1, 2	3, 4, 5, 469, 471, 473, 475, 477, 479	-
`train/assemble_disassemble_tiles_v2.1`	0, 1, 2	3, 4, 5	-
`train/assemble_jenga_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478, 480	-
`train/basic_fold-part1_v2.1`	0, 1, 2	3, 4, 5	-
`train/basic_fold-part2_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479, 481	-
`train/basic_fold-part3_v2.1`	0, 1, 2	3, 4, 5	-
`train/basic_fold-part4_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478	-
`train/basic_fold-part5_v2.1`	0, 1, 2	3, 4, 5	-
`train/basic_fold-part6_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479	-
`train/basic_pick_place-part1_v2.1`	0, 1, 2	3, 4, 5	-
`train/basic_pick_place-part2_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478, 480	-
`train/basic_pick_place-part3_v2.1`	0, 1, 2	3, 4, 5	-
`train/basic_pick_place-part4_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479, 481	-
`train/basic_pick_place-part5_v2.1`	0, 1, 2	3, 4, 5	-
`train/basic_pick_place-part6_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478	-
`train/boil_serve_egg_v2.1`	0, 1, 2	3, 4, 5	-
`train/braid_unbraid_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479	-
`train/build_unstack_lego_v2.1`	0, 1, 2	3, 4, 5	-
`train/charge_uncharge_airpods_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478, 480	-
`train/charge_uncharge_device_v2.1`	0, 1, 2	3, 4, 5	-
`train/clean_cups_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479, 481	-
`train/clean_surface_v2.1`	0, 1, 2	3, 4, 5	-
`train/clean_tableware_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478	-
`train/clip_unclip_papers_v2.1`	0, 1, 2	3, 4	-
`train/color_v2.1`	0, 1, 2	3, 4, 5	-
`train/crumple_flatten_paper_v2.1`	0, 1, 2	3, 4, 5	-
`train/deal_gather_cards_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479	-
`train/declutter_desk_v2.1`	0, 1, 2	3, 4, 5	-
`train/dry_hands_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478, 480	-
`train/fidget_magnetic_spinner_rings_v2.1`	0, 1, 2	3, 4, 5	-
`train/flip_coin_v2.1`	0, 1, 2	3, 4, 5	-
`train/flip_pages_v2.1`	0, 1, 2	3, 4, 467, 469, 471, 473, 475, 477, 479, 481	-
`train/fold_stack_unstack_unfold_cloths_v2.1`	0, 1, 2	3, 4, 5	-
`train/fold_unfold_paper_basic_v2.1`	0, 1, 2	3, 4, 5, 468, 470, 472, 474, 476, 478	-
`train/fold_unfold_paper_origami_v2.1`	0, 1, 2	3, 4, 5	-
`train/fry_bread_v2.1`	0, 1, 2	3, 4	-
`train/fry_egg_v2.1`	0, 1, 2	3, 4, 5	-
`train/gather_roll_dice_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479	-
`train/insert_dump_blocks_v2.1`	0, 1, 2	3, 4, 5	-
`train/insert_remove_airpods_v2.1`	0, 1, 2	3, 4, 468, 470, 472, 474, 476, 478, 480	-
`train/insert_remove_bagging_v2.1`	0, 1, 2	3, 4, 5	-
`train/insert_remove_bookshelf_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479, 481	-
`train/insert_remove_cups_from_rack_v2.1`	0, 1, 2	3, 4, 5	-
`train/insert_remove_drawer_v2.1`	0, 1, 2	3, 4, 468, 470, 472, 474, 476, 478	-
`train/insert_remove_furniture_bench_cabinet_v2.1`	0, 1, 2	3, 4, 5	-
`train/insert_remove_furniture_bench_round_table_v2.1`	0, 1, 2, 1759	3, 4, 5, 467, 469, 471, 473, 475, 477, 479, 1761	-
`train/insert_remove_plug_socket_v2.1`	0, 1, 2	3, 4, 5	-
`train/insert_remove_shirt_in_tube_v2.1`	0, 1, 2	3, 4, 468, 470, 472, 474, 476, 478, 480	-
`train/insert_remove_tennis_ball_v2.1`	0, 1, 2	3, 4, 5	-
`train/insert_remove_usb_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479, 481	-
`train/insert_remove_utensils_v2.1`	0, 1, 2	3, 4, 5	-
`train/knead_slime_v2.1`	0, 1, 2	3, 4, 468, 470, 472, 474, 476, 478	-
`train/load_dispense_ice_v2.1`	0, 1, 2	3, 4, 5	-
`train/lock_unlock_key_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479	-
`train/make_sandwich_v2.1`	0, 1, 2	3, 4, 5	-
`train/measure_objects_v2.1`	0, 1, 2	3, 4, 468, 470, 472, 474, 476, 478, 480	-
`train/open_close_insert_remove_box_v2.1`	0, 1, 2	3, 4, 5	-
`train/open_close_insert_remove_case_v2.1`	0, 1, 2	3, 4, 5, 467, 469, 471, 473, 475, 477, 479, 481	-
`train/open_close_insert_remove_tupperware_v2.1`	0, 1, 2	3, 4, 5	-
`train/paint_clean_brush_v2.1`	0, 1, 2	3, 4, 468, 470, 472, 474, 476, 478	-
`train/peel_place_sticker_v2.1`	0, 1, 2	3, 4, 5	-
`train/pick_place_food_v2.1`	0, 1	2, 3, 4, 466, 468, 470, 472, 474, 476, 478	-
`train/pick_up_and_put_down_case_or_bag_v2.1`	0, 1, 2	3, 4, 5	-
`train/play_mancala_v2.1`	0, 1	2, 3, 467, 469, 471, 473, 475, 477, 479	-
`train/play_piano-part1_v2.1`	0, 1, 2	3, 4, 5	-
`train/play_piano-part2_v2.1`	0, 1	2, 3, 4, 466, 468, 470, 472, 474, 476, 478, 480	-
`train/play_reset_connect_four_v2.1`	0, 1, 2	3, 4, 5	-
`train/point_and_click_remote_v2.1`	0, 1	2, 3, 467, 469, 471, 473, 475, 477	-
`train/pour_v2.1`	0, 1, 2	3, 4, 5	-
`train/push_pop_toy_v2.1`	0, 1	2, 3, 4, 466, 468, 470, 472, 474, 476, 478	-
`train/put_away_set_up_board_game_v2.1`	0, 1, 2	3, 4, 5	-
`train/put_in_take_out_glasses_v2.1`	0, 1	2, 3, 467, 469, 471, 473, 475, 477, 479	-
`train/put_toothpaste_on_toothbrush_v2.1`	0, 1, 2	3, 4, 5	-
`train/rake_smooth_zen_garden_v2.1`	0, 1	2, 3, 4, 466, 468, 470, 472, 474, 476, 478, 480	-
`train/roll_ball_v2.1`	0, 1, 2	3, 4	-
`train/scoop_dump_ice_v2.1`	0, 1	2, 3, 467, 469, 471, 473, 475, 477	-
`train/screw_unscrew_allen_fixture_v2.1`	0, 1, 2	3, 4, 5	-
`train/screw_unscrew_bottle_cap_v2.1`	0, 1	2, 3, 4, 466, 468, 470, 472, 474, 476, 478	-
`train/screw_unscrew_fingers_fixture_v2.1`	0, 1, 2	3, 4	-
`train/set_up_clean_up_chessboard_v2.1`	0, 1	2, 3	-
`train/setup_cleanup_table_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477, 479	-
`train/sleeve_unsleeve_cards_v2.1`	0, 1	2, 3, 4	-
`train/slot_batteries_v2.1`	0, 1	2, 3	-
`train/sort_beads_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476, 478	-
`train/stack_remove_jenga_v2.1`	0, 1	2, 3, 4	-
`train/stack_unstack_bowls_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477	-
`train/stack_unstack_cups_v2.1`	0, 1	2, 3	-
`train/stack_unstack_plates_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476, 478	-
`train/stack_unstack_tupperware_v2.1`	0, 1	2, 3, 4	-
`train/stack_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477, 479	-
`train/staple_paper_v2.1`	0, 1	2, 3	-
`train/stock_unstock_fridge_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476	-
`train/sweep_dustpan_v2.1`	0, 1	2, 3	-
`train/thread_unthread_bead_necklace-part1_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477	-
`train/thread_unthread_bead_necklace-part2_v2.1`	0, 1	2, 3	-
`train/thread_unthread_bead_necklace-part3_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476, 478	-
`train/throw_and_catch_ball_v2.1`	0, 1	2, 3	-
`train/throw_collect_objects_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477, 479	-
`train/tie_and_untie_shoelace_v2.1`	0, 1	2, 3	-
`train/tie_untie_rubberband_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476	-
`train/type_keyboard_v2.1`	0, 1	2, 3	-
`train/use_chopsticks_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477	-
`train/use_rubiks_cube_v2.1`	0, 1	2, 3	-
`train/vertical_pick_place-part1_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476, 478	-
`train/vertical_pick_place-part2_v2.1`	0, 1	2, 3	-
`train/vertical_pick_place-part3_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477, 479	-
`train/vertical_pick_place-part4_v2.1`	0, 1	2, 3	-
`train/vertical_pick_place-part5_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476	-
`train/vertical_pick_place-part6_v2.1`	0, 1	2, 3	-
`train/wash_fruit_v2.1`	0, 1	2, 3, 4	-
`train/wash_kitchen_dishes_v2.1`	0, 1	2, 3	-
`train/wash_put_away_dishes_v2.1`	0, 1	2, 3	-
`train/wipe_kitchen_surfaces_v2.1`	0, 1	2, 3	-
`train/wipe_screen_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477	-
`train/wrap_unwrap_food_v2.1`	0, 1	2, 3	-
`train/wrap_v2.1`	0, 1	2, 3, 466, 468, 470, 472, 474, 476, 478	-
`train/write_v2.1`	0, 1	2, 3	-
`train/zip_unzip_bag_v2.1`	0, 1	2, 3, 4, 467, 469, 471, 473, 475, 477, 479	-
`train/zip_unzip_case_v2.1`	0, 1	2, 3	-

Download and extract one task

hf download VR-VLA/VR-egodex-depth-cache-full train/clean_surface_v2.1.tar --repo-type dataset --local-dir .
tar -xf train/clean_surface_v2.1.tar

Generation

scripts/egodex/depth_batch.py (Depth-Anything-V2-Metric-Indoor-Large-hf via transformers, fp32, one frame at a time from the raw EgoDex mp4), 0.11 s/frame on one H100. Packed and uploaded per task by scripts/egodex/depth_pack_upload.py.

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EgoDex

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8 items • Updated 6 days ago