Datasets:
File size: 1,664 Bytes
7e56503 | 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 | ---
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.
|