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189-agent
Industrial 6-DoF object pose estimation (per-instance; gray+depth+mask input, CAD .ply provided; graded with ADD/VSD). Reserved for an AGENTIC setting, not VLM SFT. Category B, task T-B4, in the unified Smart-Manufacturing SFT schema.
The repository name is an internal task code. See Provenance below for the underlying dataset.
Records
123 records (validation=123).
Unified SFT schema
| field | type | meaning |
|---|---|---|
query |
str | the question / instruction (model input) |
image |
Image | the input image (bytes embedded); for multi-image rows, a preview of the first view |
images |
list[Image] | (multi-image rows) all input views / modalities for the row, bytes embedded |
annot |
str (JSON pose) | the answer — for this dataset: a JSON object {"cam_R_m2c": [9 numbers = row-major 3x3 rotation], "cam_t_m2c": [3 numbers = translation in mm]} — the target instance's 6-DoF pose ground truth, a CONTINUOUS geometric quantity graded with ADD/VSD pose-error metrics (NOT string match). images = [scene gray, scene depth (uint16 mm), the instance's visible mask]; the object's CAD model is metadata.model_ply (uploaded under assets/models/). See Task, inputs & split below |
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] | (multi-image T-B1 / D21) per-view masks aligned with images (None where a view has no defect), or multi-region masks |
Task, inputs & split
What this is. MVTec ITODD (Drost, Ulrich, Bergmann, Härtinger, Steger — Introducing MVTec ITODD: A Dataset for 3D Object Recognition in Industry, ICCV Workshops 2017) — a benchmark for industrial 3D object recognition and 6-DoF pose estimation. 28 rigid industrial parts (each with a CAD model), imaged in bins with a grayscale + depth industrial 3D sensor, in BOP format.
Task (this release): 6-DoF pose estimation. One row = one object instance. The query (our own
template) identifies the target — object obj_<id>, its 2D bounding box [x, y, w, h], and its CAD model
(metadata.model_ply) — and asks for that instance's pose. annot is a JSON object
{"cam_R_m2c": [9 numbers], "cam_t_m2c": [3 numbers]}: the 3×3 row-major rotation R and the
translation t (mm) of the object in the camera frame. This is a continuous geometric answer —
grade it with the standard ADD / VSD pose-error metrics, not string match. The instance is
specified (its bbox + visible mask are given), so localisation is an input, not part of the metric.
Inputs — images, three in this fixed order (metadata.modalities):
- gray — the scene grayscale image (8-bit, 1280×960);
- depth — the scene depth map, re-encoded to a 16-bit PNG in millimetres (multiply by
metadata.depth_scale; the original float.tifpath is inmetadata.depth_raw_tif); - mask_visib — the target instance's visible mask (0/255), locating the specified object.
The scene gray/depth are shared by every instance in that image (duplicate bytes are deduplicated in the
parquet).
image(scalar) = the gray image. The object's CAD model ships as a.plyunderassets/models/obj_<id:06d>.ply, referenced per row bymetadata.model_ply— it is what ADD/VSD needs.
Other ground truth / metadata (JSON): obj_id (1–28), 2D boxes bbox_obj / bbox_visib
([x, y, w, h]), visib_fract, pixel counts, camera intrinsics cam_K + depth_scale, and the object's
model_geometry (diameter, bbox dims in mm). obj_<id> is a specific CAD-modelled part (visually
consistent), just numbered rather than named — a well-defined fine-grained class.
Split. Only validation is included — the source's val scenes, the only ones with released
GT (54 images / 123 object instances / all 28 objects). The 721-image test set has its GT withheld
on the evaluation server and is not included.
Intended use — agentic, not SFT. A 6-DoF pose (a continuous 3x3 rotation + translation) is ill-suited
to plain text-output VLM supervised fine-tuning, so this dataset (189-agent) is reserved for an agentic
setting — e.g. a tool-using agent that calls a pose solver / renderer and is scored by ADD/VSD against
this ground truth — rather than direct model training.
Provenance
Underlying dataset: MVTec-ITODD. Upstream license: other (research use; MVTec ITODD, Drost et al., ICCV Workshops 2017) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion code under 189/ (with publish/push_to_hf.py) in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
Only the val split (54 images / 123 instances) ships GT; the 721-image test set has GT withheld upstream and is excluded. 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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