README.md

metadata

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: 189-agent
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.

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):

1. gray — the scene grayscale image (8-bit, 1280×960);
2. depth — the scene depth map, re-encoded to a 16-bit PNG in millimetres (multiply by metadata.depth_scale; the original float .tif path is in metadata.depth_raw_tif);
3. 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 .ply under assets/models/obj_<id:06d>.ply, referenced per row by metadata.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.