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D11
Factory/logistics scene object detection (COCO bbox; 5 classes). Category B, task T-B5b, in the unified Smart-Manufacturing SFT schema.
The repository name is an internal task code. See Provenance below for the underlying dataset.
Records
5,097 records (train=2820 · validation=2277).
Unified SFT schema
| field | type | meaning |
|---|---|---|
query |
str | the question / instruction (model input) |
image |
Image | the input image (bytes embedded) |
annot |
str | the answer — for this dataset: one line per detected object, class,[x, y, width, height] — exactly what the query asks for. Coordinates are NATIVE-pixel COCO xywh: top-left origin, [x, y, width, height] at the original image resolution — NOT xyxy, NOT normalized. Most grounding-capable VLMs use a different convention (norm-1000 xyxy, y-first PaliGemma/Gemini orders, Qwen2.5-VL resized-absolute); convert per student model before grounding-training — see common/box_convert.py in AI4Manufacturing/forge_model |
reasoning |
null | no native CoT in these datasets |
cate |
"B" | SFT category |
task |
"T-xx" | unified task id |
metadata |
str (JSON) | split, provenance, image_path, image_sha256 (dedup key) |
mask |
Image | null | (T-B1/T-B2 only) the pixel ground-truth mask, bytes embedded |
masks |
list[Image] | (D21 only) multi-region masks |
Provenance
Underlying dataset: LOCO (tum-fml). Upstream license: CC0 1.0 (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: D11/convert_d11.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
Shares its image pool with the derived VQA set AI4Manufacturing/D11-QA-annotated (3,609 of these 5,097 images carry QA records; as of D11-QA v2, 2026-07-07, both datasets use the official LOCO v1 split, so train/validation sides agree — training on one set's train split never touches the other's validation images). Official LOCO v1 split (subsets 1&4=validation, 2/3/5=train); each record's source subset is in metadata.subset. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.
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