Download and Setup
Return to the dataset card for coverage and an initial download example.
The download entry point is insailab/OccStress. The package index lists archive paths, availability, file counts, unpacked sizes and archive SHA256 values. Each archive is independent, not a split volume; extract selected archives into the same root. Asset shards target 2 GiB before compression and are grouped by dataset, track and upstream source.
Verified Downloads From the Code Checkout
From the OccStress code checkout, this workflow selects an immutable dataset revision, downloads only the requested track, and verifies archive sizes and SHA256 checksums:
python -m pip install -e '.[download]'
python tools/list_data_packages.py --fetch --dataset waymo --track manual > selection.json
python tools/download_data.py --selection selection.json \
--output-dir /datasets/OccStress-download
Replace waymo with nuscenes or carla, and manual with upstream or all
as needed. Keep the selection and download receipt with the evaluation record.
Only packages marked available can be downloaded with this tool. Re-running
reuses verified archives; --verify-only checks a download without network access.
Use a separate download/extraction directory for each dataset revision.
Hub CLI Downloads
To download all published files with the Hub CLI:
python -m pip install huggingface_hub
hf download insailab/OccStress --repo-type dataset \
--local-dir /datasets/OccStress-download
For a smaller download, this example selects Waymo manual assets and the corresponding protocols/metadata. The published package index records the complete archive set for each selection.
hf download insailab/OccStress --repo-type dataset \
--local-dir /datasets/OccStress-download \
--include README.md DOWNLOAD.md FORMAT.md EXTERNAL_DATA.md DISTRIBUTION.md packages.json \
'manifests/*' 'metadata/common.tar.zst' 'metadata/OccStress-Waymo.tar.zst' \
'archives/manual/OccStress-Waymo/*.tar.zst'
Metadata packages contain all protocols for their dataset. Running an upstream or position-sweep protocol additionally requires the corresponding asset shards. Position sweeps reuse manual/upstream assets; they have no duplicate voxel packs.
Extraction and External Dependencies
Downloading files does not extract archives or obtain external GT. Use a separate directory for original dependencies:
export OCCSTRESS_DATA_ROOT=/datasets/OccStress
export OCCSTRESS_EXTERNAL_ROOT=/datasets/OccStress-external
mkdir -p "$OCCSTRESS_DATA_ROOT"
set -euo pipefail
find /datasets/OccStress-download/metadata /datasets/OccStress-download/archives \
-type f -name '*.tar.zst' -print0 | \
while IFS= read -r -d '' archive; do
tar --zstd -xf "$archive" --no-same-owner -C "$OCCSTRESS_DATA_ROOT"
done
The extraction example uses Bash, GNU tar and zstd. The archives contain
relative paths directly under meta/, protocols/, occ/ and events/; do
not strip a path component. Keep manifests/ and the documentation beside
those directories if you also want the release records in the extracted tree.
Archive checksums refer to the compressed files, not individual NPZ assets.
In the OccStress code checkout, follow EXTERNAL_DATA.md to mount authorized Occ3D GT and official info files, or prepare CARLA's canonical GT. Then check the dataset you intend to use:
python tools/prepare_external_data.py check --dataset nuscenes --trust-pickle
python tools/prepare_external_data.py check --dataset waymo --trust-pickle
python tools/prepare_external_data.py check --dataset carla --trust-pickle
These are independent commands; only run the checks for datasets you prepared. Model evaluation also requires the selected forecaster's environment and checkpoint. Upstream 3D weights are needed only to regenerate exports, not to consume the provided predictions. Use the model-specific instructions in the code release.
This is a file-based benchmark consumed by OccStress adapters, not an Arrow
table to open directly with datasets.load_dataset. Protocol PKLs must come
from a trusted source; do not unpickle arbitrary downloaded files.