Task-specific data downloads
Use project Git revision 76437eb951160603d0bfd4797212094aea67a4fa or later.
The measured sizes refer to HF data revision 523986807833b27eb2d1760d3fbc3b863a95ed2c.
Sizes use decimal GB. Local extracted data needs additional space.
A dry run reads small indices. It does not transfer data archives.
Download by task
Select a task to avoid unnecessary data transfers. The task catalog records component paths, sizes, and the data revision. The task guide lists downloads for individual datasets.
| Task | Required data | Default transfer, GB |
|---|---|---|
eval |
RGB, masks and GT meshes | 29.313 |
ss |
Observations and native SS targets | 265.667 |
slat-seg |
Observations only | 221.950 |
slat-shape |
Observations and 32³ object shape targets | 233.780 |
all-training |
All published training components | 277.497 |
Use the current project checkout for these commands. Preview the transfer before downloading:
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss --dry-run
Select the task required by this server:
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-shape
Select individual datasets with --dataset, using commas for multiple datasets.
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval --dataset picasso
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval --dataset picasso,messykitchen
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg --dataset foundationpose
Task defaults follow the published component inventory. Explicit shape requests require matching object targets before archive transfer.
SS uses the published native targets without another target encoding pass. Segmentation computes its instance codes and image features during training.
Online shape conditioning avoids a separate image-feature cache.
Evaluation metadata excludes the project README. Dataset provenance documents remain in the archive.
Model weights remain separate. Download only the assets required by the selected model.
Old commands without --task still restore all training components.
| Task | Dataset | Transfer, GB |
|---|---|---|
eval |
picasso | 0.171 |
eval |
messykitchen | 27.219 |
eval |
3dfront | 1.960 |
ss |
3dfront | 118.161 |
ss |
sage | 77.456 |
ss |
foundationpose | 70.051 |
ss |
omni6dpose | 216.880 |
slat-seg |
3dfront | 86.911 |
slat-seg |
sage | 73.280 |
slat-seg |
foundationpose | 61.758 |
slat-seg |
omni6dpose | 70.945 |
slat-shape |
3dfront | 91.760 |
slat-shape |
sage | 74.558 |
slat-shape |
foundationpose | 67.462 |
slat-shape |
omni6dpose | 72.684 |
all-training |
3dfront | 123.009 |
all-training |
sage | 78.734 |
all-training |
foundationpose | 75.755 |
all-training |
omni6dpose | 218.618 |
Evaluation sizes include the shared metadata archive for each individual dataset. Combined selections download shared metadata once. Existing archives keep their original paths and checksums. No training or evaluation result changes.
Regenerate this catalog after publishing new components:
python -m preprocess.tasks publish --repo cubec/PartProbe3D-preprocessed --output outputs/huggingface/tasks