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189

Industrial 3D object recognition & 6D pose (multi-modal gray+depth+masks; per-instance; DATA-ONLY). 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

123 records (validation=123).

Unified SFT schema

field	type	meaning
`query`	str	the question / instruction (model input)
`image`	Image	the input image (bytes embedded)
`annot`	null (data-only)	the answer — for this dataset: none — this is a DATA-ONLY release: `query` is empty and `annot` is `null` (no task framing yet). Each row is one object instance; its four images (scene gray, scene depth, instance mask, instance visible mask) are in the `images` column, and the full ground truth (6D pose `cam_R_m2c`+`cam_t_m2c`, 2D `bbox_obj`/`bbox_visib`, visibility, `cam_K`, model geometry) is in `metadata`. See Modalities, GT & possible tasks below for what this data supports (incl. our suggested 2D-detection framing)
`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

Modalities, ground truth & possible tasks

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.

This release is DATA-ONLY (no query, no annot). query is an empty string and annot is null on purpose — we ship the raw multi-modal data with rich ground truth so a task can be framed later. Only the validation split is included: it is the source's val scenes, the only ones with released ground truth (54 images / 123 object instances / all 28 objects). The source's 721-image test set has its GT withheld on the evaluation server and is not included.

One row = one object instance, with all its images. Each record is a single annotated instance and its images column holds four images 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 .tif path is in metadata.depth_raw_tif);
mask — the instance's full object mask (0/255);
mask_visib — the instance's visible mask (0/255). The scene gray/depth are shared by every instance in that image (duplicate bytes are deduplicated in the parquet). image (scalar) = the gray image.

All ground truth is in metadata (JSON): obj_id (1–28), 6D pose cam_R_m2c (3×3 row-major rotation) + cam_t_m2c (translation, mm), 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). A text VLM cannot emit a rotation matrix, so the 6D pose is kept as data/GT, not framed as a task.

Tasks this data supports.

Original benchmark task (Drost et al. 2017): 6-DoF pose estimation and 3D object detection from gray + depth (evaluated with pose-error metrics such as ADD/VSD). The pose + camera + model GT here are exactly what such a task needs.
Instance segmentation / object presence & counting: the per-instance masks + obj_id list support segmentation and "which/how many of the 28 parts are in this bin".
2D object detection — suggested by us (this project), NOT part of the original benchmark. Because the 6D pose is not text-friendly, a natural VLM framing is to detect each instance's 2D box: input the gray (+ optionally depth), output one line per instance obj_<id>,[x, y, w, h] using bbox_visib. Here obj_<id> is a specific CAD-modelled part (visually consistent, so learnable), just numbered rather than named. This framing is our suggestion for downstream SFT use; it is not an official ITODD task and is not baked into this release (which stays data-only).

Note on the object ids. The 28 objects are identified by number (obj_1..obj_28); ITODD does not give them semantic names. Unlike an arbitrary code, each id is one fixed physical part (with a CAD model), so it is a well-defined fine-grained class.

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 script: 189/convert_d89.py, published with publish/push_to_hf.py, both 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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