README.md

---
license: cc-by-4.0
pretty_name: Detecture ICLR Benchmarking
task_categories:
  - image-segmentation
tags:
  - texture-segmentation
  - sub-semantic-segmentation
  - referring-segmentation
  - benchmark
size_categories:
  - 1K<n<10K
---

# Detecture ICLR Benchmarking

The four evaluation routes reported in the ICLR 2027 submission on sub-semantic
image segmentation, bundled so the published numbers can be reproduced from a
single download.

This is a **smaller, paper-aligned** release. An earlier bundle,
[`aviadcohz/Detecture_Benchmarking`](https://huggingface.co/datasets/aviadcohz/Detecture_Benchmarking),
carried five datasets at 4.3 GB for a previous version of this work. That one
included routes the current paper does not report. This release carries only what
the paper evaluates on.

| Directory | Paper label | Images | Regions / image | Source |
|---|---|---:|---:|---|
| `RWTD/` | RWTD | 253 | 2 | Real-world texture photographs |
| `CAP/` | RWTD-COCO | 256 | 2 | COCO-Stuff, by deterministic annotation reuse |
| `ADE20k_Detecture/` | TextureADE | 212 | 3.91 avg | Mined from the ADE20K validation split |
| `CSTD/` | CSTD | 256 | 2 by construction | Synthetic, SD-1.5 + ControlNet over DTD textures |

**The directory names are the on-disk names the evaluation code expects, not the
paper's display labels.** They are deliberately left unrenamed: clone this repo
into `~/datasets/` and the evaluation configs resolve every route without edits.
The table above is the mapping.

## Layout

```
RWTD/  CAP/  ADE20k_Detecture/
├── images/              RGB images
├── masks/               per-image region maps
├── textures_mask/       per-texture binary masks, <id>_mask_<k>.png
├── overlays/            image + mask visualisations (not ground truth)
├── metadata.json        image paths, mask paths, descriptions
└── summary.json         per-dataset statistics

CSTD/
├── images/              256 generated images
├── textures_mask/       512 binary masks, two per image
├── metadata.json        the 256-image evaluation subset
├── verified_256_ids.json  the accepted ids
├── screen_scores.json   per-image screening metrics for all 10,000
├── screen_rank.json     the ranking those scores induce
└── screen_cstd.py       the screening script, so the selection is reproducible
```

Paths inside every `metadata.json` are relative to the repository root, so the
bundle can be placed anywhere.

## Read this before using CSTD

CSTD is redistributed here as a **256-image hand-verified subset**, not as
published. The original release is on Kaggle as
`architexanonymous/cstd-controlnet-synthetic-texture`.

The reason is a ground-truth problem. CSTD's released `regions/*.png` is the
stitching mask fed *into* ControlNet, not an annotation of what came out. Where
the generator invented a third material or drifted from the mask, the ground
truth silently stops describing the image. Three failure modes were observed and
confirmed by eye: a region containing two distinct textures, a third material
appearing at an edge or corner, and a contour that does not sit on any real
appearance change.

All 10,000 images were therefore screened on texture features, ranked, and the
top candidates reviewed by eye. **274 of 1,296 reviewed candidates were accepted,
a 21% pass rate**, and the top 256 form this subset. `screen_cstd.py` and
`screen_scores.json` are included so the screening is reproducible rather than
asserted.

Anyone evaluating on CSTD as published will get different numbers, and should.

## Download

```bash
cd ~/datasets
git lfs install
git clone https://huggingface.co/datasets/aviadcohz/Detecture_ICLR_Benchmarking .
```

Or from Python:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="aviadcohz/Detecture_ICLR_Benchmarking",
    repo_type="dataset",
    local_dir="~/datasets",
)
```

## Provenance

**RWTD-COCO** carries no predicted pixels. A whitelist of 28 surface-like
COCO-Stuff classes proposes adjacent label pairs, crops are enumerated around the
shared boundary and scored by a closed-form structural criterion, and every
ground-truth pixel is a deterministic remap of existing human annotation. No SAM,
CLIP, DINO or saliency model participates in its construction.

**TextureADE** is mined from the natural ADE20K validation split by a
geometry-first scoring procedure: acceptance rests on observable mask geometry,
and a frozen vision-language annotator is queried only afterwards, against a
region that has already been accepted.

**RWTD** is redistributed unchanged as a cross-domain stress test.

## Evaluation protocol

Every number in the paper comes from one protocol, applied identically to every
method and route: no ground-truth region count in the prompt, no inverse-mask
completion, no truncation of proposals to a known count, and no dropping of
images where a method returns nothing. Region counts are inferred, never
supplied.

## Licence

CC-BY-4.0 for this bundle. Upstream corpora keep their own licences: ADE20K,
COCO-Stuff and DTD are each governed by their original terms, and this release
does not relicense them.