AI4Manufacturing/DD1_1

DD1 OT VQA Grounding

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

Schema

field	type	meaning
query	string	one of 34 deterministic LPBF OT grounding prompts
image	Image	original 2000×2000 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. Boxes are sorted by x, then y. Width and height are derived as xmax-xmin and ymax-ymin.

Counts

• Records: 2667
• Positive images: 1122
• Good/negative images: 1545
• Total boxes: 4339
• 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_ot_vqa.py \
  --source /path/to/DD1_cleaned_grounding \
  --output /path/to/DD1_OT_VQA_grounding

The build is deterministic and refuses to overwrite an existing output.

Disclosure

Good means no author-annotated overheated region under the source labeling rule; it does not claim absence of every possible manufacturing defect. OT bbox scale varies across layers and may mix local and larger-region annotations.