README.md

metadata

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

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

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.