card: annot coordinate-convention note (native-px COCO xywh; convert per student model)
0196dae verified | tags: | |
| - smart-manufacturing | |
| - sft | |
| - industrial | |
| - vision | |
| license: other | |
| pretty_name: D11 | |
| extra_gated_fields: | |
| Name: text | |
| Affiliation: text | |
| Intended use: text | |
| extra_gated_prompt: >- | |
| This dataset is released for **research use**. Access is reviewed and granted | |
| **manually** by the maintainers. Please state your name, affiliation, and intended use. | |
| # 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`](https://github.com/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`](https://github.com/AI4Manufacturing/forge_model). | |
| ## Overlap / de-duplication (§8) | |
| Shares its image pool with the derived VQA set [`AI4Manufacturing/D11-QA-annotated`](https://huggingface.co/datasets/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. | |