| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: "210" |
| 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. |
| --- |
| |
|
|
| <!-- ROLES-CANON:BEGIN --> |
| ## Roles |
|
|
| **Roles:** canon repo — `annot` is the source label, kept machine-parseable as the gold for verification and reward parsing; there is no `reasoning` column and this repo is **not itself a training view**. Derived repos (`-annotated`, `-grounding`, `-region`, `-mcq`) each state their own regime on their own card. Geometry for every record lives in `metadata.geometry` (below). |
| <!-- ROLES-CANON:END --> |
| # 210 |
|
|
| Outdoor maintenance-inspection anomaly detection over 7 asset scenarios (SYNTHETIC, 3D-rendered; binary masks). Category **B**, task **T-B1**, in the unified Smart-Manufacturing SFT schema. |
|
|
| > The repository name is an internal task code. See **Provenance** below for the underlying dataset. |
|
|
| ## Records |
|
|
| **105,000** records (test=35000 · train=70000). Pixel masks are embedded as a `mask` image column. |
|
|
| ## 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 | the answer — for this dataset: plain-text `{label, defect_type}` — `{good, null}` or `{anomalous, <defect>}`, the defect name from THAT scenario's own closed set (enumerated in the query), following D20/D22. The binary `mask` column is deferred localization GT | |
| | `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, mask & split |
|
|
| **What this is.** MIAD — Maintenance Inspection Anomaly Detection (Bao, Chen, Li, Wang, Fei, Wu, Zhao, Zheng, |
| arXiv:2211.13968; ICCV 2023 Workshop) — **105,000** 512x512 images across **7 outdoor maintenance-inspection |
| scenarios**: `catenary_dropper`, `electrical_insulator`, `metal_welding`, `nut_and_bolt`, `photovoltaic_module`, |
| `wind_turbine`, `witness_mark`. The design is perfectly regular: **every** scenario ships 10,000 good training |
| images and a 5,000-image test split (2,500 good + 2,500 defective) with 2,500 pixel masks. |
|
|
| These are **outdoor assets in service** — overhead lines, turbine blades, PV modules — not factory production-line |
| parts, which is what most of this corpus holds. |
|
|
| **⚠ THE IMAGERY IS SYNTHETIC.** MIAD is generated with 3D graphics software, not photographed. That is the point of |
| the dataset — it buys free variation in viewpoint, weather and lighting together with exact pixel ground truth — |
| but a model trained on it learns *rendered* appearance, and **any claim about real-world transfer needs a real-image |
| test set**. Every record carries `metadata.synthetic = true` so a training mixture can weight or exclude it. The |
| only other synthetic member of this corpus is 182 (Eyecandies); everything else is photographic. |
|
|
| **Task & answer.** Anomaly detection with defect naming. `query` is our own template (the source ships no |
| natural-language question): it names the asset and asks whether it is **good** or **anomalous** and, if anomalous, |
| for the defect type from that scenario's closed set. `annot` is plain text `{good, null}` / `{anomalous, <defect>}`. |
|
|
| **Defect vocabularies differ per scenario, and three are effectively binary.** `electrical_insulator` (`broken`), |
| `wind_turbine` (`crack`) and `witness_mark` (`looseness`) have exactly one defect type, so naming it adds nothing |
| beyond the label there; `metadata.single_defect_type` marks those records. The four richer scenarios are |
| `catenary_dropper` (broken / looseness / miss), `nut_and_bolt` (looseness / missbolt / missnut), |
| `photovoltaic_module` (broken / foreign_body / miss) and `metal_welding` (weld_beading / weld_pit). |
|
|
| **Mask (deferred GT).** Binary `{0, 255}` masks are embedded in the `mask` column for all 17,500 defective test |
| images; good images carry `mask = null`. |
|
|
| **Lazy-baseline floor.** The test split is **exactly balanced** — 17,500 good vs 17,500 anomalous — so the binary |
| majority floor is **50.0%**, and the full `{label, defect_type}` floor is also **50.0%** (answering `{good, null}` |
| every time). This is one of the cleanest floors in the corpus, a consequence of the synthetic design. |
|
|
|
|
| ## Provenance |
|
|
| Underlying dataset: **MIAD (Maintenance Inspection Anomaly Detection)**. Upstream license: **CC BY-NC-SA 4.0 (non-commercial)** (this card is `license: other`; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion code under `210/` (with `publish/push_to_hf.py`) in [`AI4Manufacturing/forge_model`](https://github.com/AI4Manufacturing/forge_model). |
|
|
| ## Overlap / de-duplication (§8) |
|
|
| No overlap with any other dataset in this corpus. ⚠ The imagery is **3D-rendered, not photographed** — see the synthetic note below. Each record carries `metadata.image_sha256` so overlapping images can be kept entirely on one side of a train/eval split. |
|
|
| <!-- GEOMETRY-BLOCK:BEGIN --> |
| ## Geometry (`metadata.geometry`) |
|
|
| Every record carries a **`geometry`** block inside the existing `metadata` JSON string, so that its |
| gold can be **re-derived at any render size**. No schema column changed; existing loaders are |
| unaffected. |
|
|
| Coordinates are **native pixels** of the image in that record (`coords_frame: "record_image"`). `scale` is `1.0` throughout — this repo publishes at source resolution, nothing was downscaled at publish time. |
|
|
| ```jsonc |
| "geometry": { |
| "image_wh": [W, H], // dims of the image in THIS record |
| "source_wh": [W, H], // dims of the original source image |
| "scale": 1.0, // image_wh / source_wh; < 1.0 would disclose a publish-time downscale |
| "n_instances": 2, |
| "instances": [ |
| { "instance_id": 1, "bbox_xywh": [x, y, w, h], "min_side_px": 65, "class": null } |
| ], |
| "n_dropped_subminimum": 0, // components removed by the filters below |
| "union_box_fallback": false,// true => boxes are per-class unions, NOT real instances |
| "conventions": { ... } // see table |
| } |
| ``` |
|
|
| **`instances` is present even when empty.** `[]` means the record genuinely has no defects; an |
| *absent* block would mean geometry could not be recovered. Those are different states and are never |
| conflated. |
|
|
| ### Conventions used to derive it |
|
|
| There is no universal definition of "one defect instance" — it depends on the mask the source |
| shipped. This repo's is stated, not implied: |
|
|
| | field | value | |
| |---|---| |
| | `algorithm` | `dilate_cc` | |
| | `binarisation` | `gt:0` | |
| | `connectivity` | `4` | |
| | `merge` | `mask_dilate:1pct` | |
| | `min_area_px` | `15` | |
| | `max_instances` | `None` | |
| | `artifact` | `fine` | |
| | `fill_floor` | `None` | |
| | `legibility_floor_px` | `None` | |
| | `min_side_floor_px` | `None` | |
| | `spec_sha` | `41d7fab342f60aca` | |
|
|
| ### Provenance and verification |
|
|
| | | | |
| |---|---| |
| | records | 105,000 | |
| | carrying a geometry block | **105,000 / 105,000** | |
| | instances per record | `0`: 87,500, `1`: 11,395, `2`: 4,143, `3`: 958, `4`: 606, `5+`: 398 | |
| | total instances | 27,172 | |
| | image dimensions | 512×512 (105,000) | |
| | `scale` values present | [1.0] | |
|
|
| Computed from this repo's own masks and **verified against this repo's own published answers before it was written** — a |
| recomputation that disagreed with the shipped gold would have aborted the update rather than |
| overwritten it. |
|
|
| ### ⚠ The 16px floor applies at the RENDER, not at native |
|
|
| `min_side_px` is in **native** pixels. The model does not see native: Qwen2-VL caps by megapixels |
| AND snaps each dimension to a multiple of 28. So **`min_side_px >= 16` is the floor tested in the |
| wrong frame.** Measured on this repo: |
|
|
| | | | |
| |---|---| |
| | native → rendered (qwen2_vl @ 2.36MP) | 512×512 → 504×504 | |
| | shipped boxes | 27,172 | |
| | **legible at that render (>=16px there)** | **18,339 (67.5%)** | |
| |
| ⚠ An earlier version of this section reported the inverse — boxes clearing 16px at native and failing |
| at the render — and that number was **misleading**. It is frame-relative: publishing at a larger |
| native size lets more boxes clear 16 *in the published frame*, so more can "fail", which penalises |
| exactly the choice that helps. Measured on 179: publishing native (3024) means a box needs **>=32px |
| native** to be legible at the render and **86.7%** qualify; the previous 1024 publish needed **>=47px |
| native** and only **69.5%** qualified. The native republish improved rendered legibility by 17 points |
| while the old metric scored it as 12.5% "broken". The figure above is the comparable one. |
| |
| Nothing in the data is frame-dependent — geometry is native and complete. Use |
| `forge_model/common/adapt_engine.py`, which applies the floor at whatever size the consumer renders. |
| |
| ### Using it |
| |
| Coordinates only stay correct if they are rescaled with the image. A patch-based VLM does **not** |
| render at native size: Qwen2-VL's processor snaps both dimensions to a multiple of 28, so this repo's |
| 512×512 is rendered 504×504 and native-pixel boxes are then wrong by a few pixels. |
| `forge_model/common/adapt_engine.py` regenerates coordinates for a target render size, re-derives counts, and drops records whose |
| gold no longer holds there. |
| <!-- GEOMETRY-BLOCK:END --> |
| |
