---
pretty_name: DD1 PB VQA Grounding
tags:
  - visual-question-answering
  - visual-grounding
  - industrial
  - additive-manufacturing
  - sft
task_categories:
  - visual-question-answering
---

# DD1 PB VQA Grounding

Answer-only VQA-style grounding data derived deterministically from the PB
portion of `DD1_cleaned_grounding`.

## Schema

| field | type | meaning |
|---|---|---|
| `query` | string | one of 34 deterministic LPBF PB grounding prompts |
| `image` | Image | original 1280×1024 JPEG bytes; never cropped |
| `annot` | string | JSON list `[{"bbox_xywh":[x,y,w,h]}]`, or `[]` |
| `reasoning` | null | answer-only dataset |
| `cate` | string | `B` |
| `task` | string | `T-B1` |
| `metadata` | string | JSON provenance, hashes, boxes and disclosures |

Coordinates use native pixels with top-left origin. Width and height are
`xmax-xmin` and `ymax-ymin`.

## Counts

- Records: 2637
- Positive images: 1529
- Good/negative images: 1108
- Total boxes: 5000
- Query variants: 34
- Split: train only

## Load

```python
from datasets import load_dataset
ds = load_dataset(
    "parquet",
    data_files={"train": "data/train-00000-of-00001.parquet"},
)
```

`annot` is the direct SFT answer. `reasoning` is null on every row.

## Reproduce

```bash
python3 -m pip install -r requirements.txt
python3 build_dd1_pb_vqa.py \
  --source /path/to/DD1_cleaned_grounding \
  --output /path/to/DD1_PB_VQA_grounding
```

## Disclosure

The source uses the generic class label `defects` without a subtype taxonomy.
`Good` means no author-annotated PB defect under the source labeling rule; it
does not guarantee absence of every possible manufacturing defect.