---
license: cc-by-nc-4.0
pretty_name: Qwen-camo
task_categories:
- image-segmentation
- mask-generation
language:
- en
size_categories:
- 1K<n<10K
tags:
- camouflaged-object-detection
- background-removal
- image-matting
- language-guided-segmentation
- referring-segmentation
- machine-generated-annotations
configs:
- config_name: default
  data_files:
  - path: data/train-*
    split: train
  - path: data/test-*
    split: test
dataset_info:
  features:
  - dtype: string
    name: image_name
  - dtype: image
    name: image
  - dtype: image
    name: mask
  - dtype: string
    name: prompt
  - dtype: int32
    name: prompt_tokens
  - dtype: string
    name: prompt_raw
  splits:
  - name: train
    num_examples: 1000
  - name: test
    num_examples: 250
---

# Qwen-camo

[CAMO](https://huggingface.co/datasets/nobg/camo) images and masks, plus a **short
concept name per image** generated by
[`Qwen/Qwen3.6-27B-FP8`](https://huggingface.co/Qwen/Qwen3.6-27B-FP8), so
prompt-conditioned segmentation on camouflaged subjects needs one `load_dataset` call
instead of a generic placeholder prompt. **1250 rows, 2 splits.**

```python
from datasets import load_dataset

ds = load_dataset("nobg/Qwen-camo", split="test")
ds[0]["image"]          # PIL, original resolution, byte-identical to nobg/camo
ds[0]["mask"]           # PIL, the binary mask
ds[0]["prompt"]         # 'a leaf-tailed gecko'
ds[0]["prompt_tokens"]  # 7 -- CLIP BPE count, guaranteed <= 32
```

## Why this exists

Camouflage is the hardest case for an open-vocabulary segmenter, because the subject is
*by construction* hard to name — and it was the case with no name available. In
[`nobg`](https://github.com/feyninc/nobg)'s SAM 3 training mix, CAMO carried an empty
prompt field, meaning all 1250 images fell back to one run-level phrase
(`"the main foreground subject"`). This dataset supplies the per-image concept instead.

## How the prompts were made

Each image was composited **subject-only onto a flat neutral grey background** and shown
to `Qwen/Qwen3.6-27B-FP8` with an instruction asking for a 1–4 word noun phrase. Greedy
decoding (`temperature=0`), so the annotation is reproducible.

The compositing detail matters and is easy to get wrong: a straight-alpha RGBA cutout is
a **no-op** for a vision-language model, because every image processor calls
`.convert("RGB")` internally, which discards the alpha and restores the original pixels.
The model would then describe the unmasked scene — for camouflage, that means naming the
*habitat* instead of the animal, plausibly enough that nothing looks wrong. The
background is therefore explicitly pasted, and the build verified this by annotating a
sample both ways and confirming the two disagree.

| column | type | contents |
|:--|:--|:--|
| `prompt` | `string` | the cleaned concept name, `≤ 32` CLIP tokens. `""` where the model refused or named nothing usable |
| `prompt_tokens` | `int32` | measured CLIP BPE count, so the bound is auditable from inside the data |
| `prompt_raw` | `string` | the model's verbatim output, before normalization |

Token counts: median **5**, max **11**, budget 32.
**592** distinct concepts over 1250 prompts.
0 rows have an empty `prompt`.

An empty `prompt` is a deliberate fallback value, not a hole: `nobg`'s loader treats it
as "use the run-level prompt", so a refusal degrades to the previous behaviour instead of
training on a wrong name.

## Limitations — read before training on this

- **The prompts are machine-generated and unreviewed.** No human checked 1250
  annotations. Expect errors, especially in species-level naming.
- **They describe the masked cutout, not the scene.** For camouflage this can differ from
  what a human naming the photograph would say: a subject that is only recognizable *in
  context* may be named more generically once isolated.
- Images and masks are **byte-identical** to `nobg/camo` — no re-encode or resize. Only
  the two 400×300 `overlaid_mask_*` thumbnail columns were dropped.

## Licensing

**`cc-by-nc-4.0`**, inherited from [`nobg/camo`](https://huggingface.co/datasets/nobg/camo)
— non-commercial. `Qwen/Qwen3.6-27B-FP8` is Apache-2.0, so the generated text carries no
additional restriction, but the images govern.

These prompts are **unrelated to the FlowDIS paper**; the name follows the
`<method>-<source>` convention of [`nobg/FlowDIS-DIS5K`](https://huggingface.co/datasets/nobg/FlowDIS-DIS5K).

## Citation

Images and masks — CAMO:

```bibtex
@article{le2019anabranch,
  title={Anabranch network for camouflaged object segmentation},
  author={Le, Trung-Nghia and Nguyen, Tam V and Nie, Zhongliang and Tran, Minh-Triet and Sugimoto, Akihiro},
  journal={Computer Vision and Image Understanding},
  volume={184},
  pages={45--56},
  year={2019}
}
```