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Return to the [dataset card](README.md) for coverage and an initial download example.
The download entry point is
[insailab/OccStress](https://huggingface.co/datasets/insailab/OccStress).
The [package index](packages.json) 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:
```bash
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:
```bash
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.
```bash
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:
```bash
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](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:
```bash
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.
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