File size: 3,936 Bytes
1f30b22 090648d 1f30b22 0332166 1f30b22 0432c00 1f30b22 0432c00 1f30b22 584dfd7 1f30b22 0332166 1f30b22 f7b17dc 1f30b22 f7b17dc 1f30b22 0432c00 f7b17dc 1f30b22 0432c00 f7b17dc 1f30b22 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # 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.
<!-- partprobe3d-tasks:start -->
## Download by task
Select a task to avoid unnecessary data transfers.
The [task catalog](index.json) records component paths, sizes, and the data revision.
The [task guide](README.md) lists downloads for individual datasets.
| Task | Required data | Default transfer, GB |
| --- | --- | ---: |
| [`eval`](eval/index.json) | RGB, masks and GT meshes | 29.313 |
| [`ss`](ss/index.json) | Observations and native SS targets | 265.667 |
| [`slat-seg`](slat-seg/index.json) | Observations only | 221.950 |
| [`slat-shape`](slat-shape/index.json) | Observations and 32³ object shape targets | 233.780 |
| [`all-training`](all-training/index.json) | All published training components | 277.497 |
Use the current project checkout for these commands.
Preview the transfer before downloading:
```bash
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss --dry-run
```
Select the task required by this server:
```bash
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.
```bash
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.
<!-- partprobe3d-tasks:end -->
| 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:
```bash
python -m preprocess.tasks publish --repo cubec/PartProbe3D-preprocessed --output outputs/huggingface/tasks
```
|