README.md

---

license: apache-2.0
pretty_name: FlowDIS-DIS5K
task_categories:
- image-segmentation
- mask-generation
language:
- en
size_categories:
- 1K<n<10K
tags:
- dichotomous-image-segmentation
- dis
- background-removal
- image-matting
- language-guided-segmentation
- referring-segmentation
configs:
- config_name: default
  data_files:
  - split: DIS_TR
    path: data/DIS_TR-*
  - split: DIS_VD
    path: data/DIS_VD-*
  - split: DIS_TE1
    path: data/DIS_TE1-*
  - split: DIS_TE2
    path: data/DIS_TE2-*
  - split: DIS_TE3
    path: data/DIS_TE3-*
  - split: DIS_TE4
    path: data/DIS_TE4-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype: image
  - name: image_name
    dtype: string
  - name: label_name
    dtype: string
  - name: prompts
    list: string
  - name: prompt
    dtype: string
  - name: prompt_keywords
    dtype: string
  - name: flowdis_split
    dtype: string
  splits:
  - name: DIS_TR
    num_bytes: 3105875783.0
    num_examples: 3000
  - name: DIS_VD
    num_bytes: 479868844.0
    num_examples: 470
  - name: DIS_TE1
    num_bytes: 405786175.0
    num_examples: 500
  - name: DIS_TE2
    num_bytes: 487220171.0
    num_examples: 500
  - name: DIS_TE3
    num_bytes: 600557987.0
    num_examples: 500
  - name: DIS_TE4
    num_bytes: 676477543.0
    num_examples: 500
  download_size: 5731293690
  dataset_size: 5755786503.0
---


# FlowDIS-DIS5K

[DIS5K](https://huggingface.co/datasets/nobg/DIS5K) images and masks joined to the
language prompts of
[`PAIR/FlowDIS-DIS5K-language-prompts`](https://huggingface.co/datasets/PAIR/FlowDIS-DIS5K-language-prompts),
so language-guided dichotomous segmentation needs one `load_dataset` call instead of a
join. **5470 rows, all six DIS5K splits.**

```python

from datasets import load_dataset



ds = load_dataset("nobg/FlowDIS-DIS5K", split="DIS_VD")

ds[0]["image"]            # PIL, original resolution

ds[0]["label"]            # PIL, the binary mask

ds[0]["prompt"]           # 'Four white wind turbines with red markings ...'

ds[0]["prompt_keywords"]  # the short form -- see below

```

## Why this exists: the upstream `test` split collapses four splits into one

HuggingFace infers splits from *file names*, and FlowDIS ships one JSON per DIS5K
split. Four of the six names collapse:

| upstream file | inferred split | rows |
|:--|:--|--:|
| `DIS-TR.json` | `train` | 3000 |
| `DIS-VD.json` | `validation` | 470 |
| `DIS-TE1.json` … `DIS-TE4.json` | **`test` — all four merged** | 2000 |

So `load_dataset("PAIR/FlowDIS-DIS5K-language-prompts")["test"]` is a 2000-row mixture of four splits of
**graded difficulty** (DIS-TE1 easiest → DIS-TE4 hardest) — precisely the axis DIS5K's
test splits exist to separate. It reports one number where the benchmark defines four,
and nothing warns you.

This dataset keys off each row's own `split` field instead, and uses **nobg/DIS5K's six

split names**, so it lines up 1:1 with the image source.

## The join is exact

`filename` (FlowDIS) matches `image_name` (DIS5K) byte-for-byte, taxonomy prefix
included — `22#Weapon#3#Missile#11711594126_3831657d91_o.jpg`. Measured across all six
splits: **5470 / 5470 matched, 0 unmatched, 0 duplicates.** No normalization or
fuzzy matching. The build asserts the tally per shard and refuses to write a
partially-joined split, so an empty `prompt` cannot occur.

Image and label bytes are **copied through undecoded** — pixels are bit-identical to
`nobg/DIS5K`, with no re-encode or resize.

## The three prompt columns

Phrasings per row differ by split, and only `DIS_TR` has more than one:

| split | rows | phrasings/row |
|:--|--:|--:|
| `DIS_TR` | 3000 | **4** |
| `DIS_VD` | 470 | 1 |
| `DIS_TE1`–`DIS_TE4` | 500 each | 1 |

| column | type | contents |
|:--|:--|:--|
| `prompts` | `list<string>` | **all** phrasings, verbatim — lossless |
| `prompt` | `string` | `prompts[0]`, the full descriptive sentence |
| `prompt_keywords` | `string` | `prompts[1]` where it exists, else `prompts[0]` |

In `DIS_TR`, index 0 is a descriptive sentence, index 1 a comma-separated keyword form,
and 2–3 paraphrases of 0:

```

prompt          'A silver missile mounted on a large green military transport vehicle.'

prompt_keywords 'Missile, military launcher vehicle'

```

**`prompt_keywords` is not cosmetic.** SAM 3's processor tokenizes at

`padding="max_length", max_length=32`, so descriptive sentences truncate silently while

the keyword form fits. Note the trade-off in its fallback: on the five eval splits

(1 phrasing each) it is **identical to `prompt`**. Stated plainly because a column that

is sometimes a copy is otherwise a trap.



`flowdis_split` records the upstream label each row declared, so the collapse above

stays auditable from inside the data.



## Licensing — read before redistributing



The upstream prompt repo **declares no license**. The prompts are redistributed here on

that basis, with attribution below; images and masks come from `nobg/DIS5K`

(Apache-2.0, itself subject to DIS5K's own Terms of Use). Treat the prompts as a soft

dependency: [`nobg`](https://github.com/feyninc/nobg)'s training script keeps

`--flowdis` **off by default** and falls back to a concept derived from the filename
taxonomy, and anything built on this should preserve that property.

## Citation

Prompts — FlowDIS:

```bibtex

@article{sargsyan2026flowdis,

  title={{FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching}},

  author={Sargsyan, Andranik and Navasardyan, Shant},

  journal={arXiv preprint arXiv:2605.05077},

  year={2026},

  url={https://arxiv.org/abs/2605.05077}

}

```

Images and masks — DIS5K:

```bibtex

@inproceedings{qin2022dis,

  title={Highly Accurate Dichotomous Image Segmentation},

  author={Qin, Xuebin and Dai, Hang and Hu, Xiaobin and Fan, Deng-Ping and Shao, Ling and Van Gool, Luc},

  booktitle={ECCV},

  year={2022}

}

```