Datasets:
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license: cc-by-4.0
pretty_name: TextureADE
task_categories:
- image-segmentation
tags:
- texture-segmentation
- sub-semantic-segmentation
size_categories:
- n<1K
---
# TextureADE
Real scenes carrying several appearance transitions each, mined from the ADE20K validation split.
One of the four evaluation routes in the ICLR 2027 submission on sub-semantic
image segmentation: partitioning an image into regions that are coherent in
appearance and describable in language, but that need not correspond to any
object, part or material class.
- **Images:** 212
- **Code:** [github.com/aviadcohz/Qwen2SAM_Detecture_Benchmark](https://github.com/aviadcohz/Qwen2SAM_Detecture_Benchmark)
- **Weights:** [aviadcohz/Detecture-ICLR-2027](https://huggingface.co/aviadcohz/Detecture-ICLR-2027)
- **All four routes in one download:** [aviadcohz/Detecture_ICLR_Benchmarking](https://huggingface.co/datasets/aviadcohz/Detecture_ICLR_Benchmarking)
## Layout
```
ADE20k_Detecture/
├── images/ RGB images
├── textures_mask/ per-texture binary masks, <id>_mask_<k>.png
├── metadata.json image paths, mask paths, descriptions
└── summary.json dataset statistics
```
The three real-world routes also carry `masks/` and `overlays/`; overlays are visualisations, not ground truth.
The directory inside this repo is named `ADE20k_Detecture` rather than `TextureADE`, because
that is the name the evaluation configs resolve
(`fairness_baseline_suite/src/paths.py`). Paths inside `metadata.json` are relative
to the repository root, so the folder can be placed anywhere.
## Use
```bash
cd ~/datasets
git lfs install
git clone https://huggingface.co/datasets/aviadcohz/TextureADE
mv TextureADE/ADE20k_Detecture . && rm -rf TextureADE
```
Then, from the benchmark repo:
```bash
cd Qwen2SAM_Detecture_Benchmark/fairness_baseline_suite
PYTHONPATH=src python src/run_fairness.py --model detecture --dataset TextureADE
```
## Evaluation protocol
Every number reported on this route comes from one protocol applied identically to
every method: 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. The region count is inferred, never supplied.
Results obtained this way are **not** comparable to evaluations that supply it.
## Provenance
Mined from the natural ADE20K validation split by a geometry-first procedure:
connected components are merged into at most five candidate regions each covering
at least 1% of image area, scored on mask structure and boundary geometry, and
admitted only above a fixed threshold. A frozen vision-language annotator is
queried afterwards, against a region that has already been accepted, so language
never proposes regions.
## Licence
CC-BY-4.0 for this packaging. Upstream corpora keep their own terms: ADE20K.
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