Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
VLNCE-EnvDrop
Synthetic Vision-Language Navigation (VLN) data-augmentation set, derived from the EnvDrop augmentation used in VLN-CE / NaVILA-style training. Each of the 146,304 samples pairs a short first-person navigation video with the natural-language instruction the agent was following and the discrete action sequence it executed.
This dataset provides the visual + motion supervision for training a GRU-augmented
Qwen3-VL navigation model: the language conditions the backbone, while the per-step
motion sequence feeds a GRU whose output is projected into the LLM embedding space.
Contents
| File | Size | What it is |
|---|---|---|
envdrop_videos_00.tar … envdrop_videos_14.tar |
~270 GB | The raw first-person navigation videos, one <video_id>.mp4 per sample, sharded into 15 tarballs. |
envdrop_motion.json |
392 MB | Primary training annotation. One record per sample: instruction, decoded frame paths, and the per-step action (motion) sequence. |
annotations.json |
22.5 MB | Lightweight video_id → instruction index (powers the dataset preview). A subset of the info in envdrop_motion.json. |
Record schema — envdrop_motion.json
{
"video_id": "34300",
"q": "Walk forward and stop at the end of the aisle.",
"frames": ["34300/frame_0.jpg", "34300/frame_1.jpg", "..."],
"motion": [3, 3, 1, 1, 3, 1, 1, 2, 2, 1, "..."]
}
video_id— key into the tarballs (<video_id>.mp4).q— the natural-language navigation instruction.frames— decoded frame paths for the clip (frames are extracted from the corresponding.mp4at load time; they are not stored separately).motion— the discrete action taken at each step (small action vocabulary, e.g. forward / turn-left / turn-right / stop). This is the GRU input.
Layout
VLNCE-EnvDrop/
├── envdrop_videos_00.tar # <video_id>.mp4 clips
│ ... # (15 shards, ~270 GB total)
├── envdrop_videos_14.tar
├── envdrop_motion.json # primary training annotation (146,304 records)
└── annotations.json # video_id -> instruction index / preview
Usage
from huggingface_hub import snapshot_download
# annotations only (small)
snapshot_download("Rithvik762/VLNCE-EnvDrop", repo_type="dataset",
allow_patterns=["*.json"])
# full dataset incl. video tars (~270 GB)
snapshot_download("Rithvik762/VLNCE-EnvDrop", repo_type="dataset")
After download, extract the shards (e.g. for f in envdrop_videos_*.tar; do tar xf "$f"; done).
Videos and envdrop_motion.json must be kept together — the JSON references
video_ids that live inside the tarballs.
License
Released under the MIT license.
- Downloads last month
- 22