---
pretty_name: NC4K
task_categories:
- image-segmentation
- mask-generation
size_categories:
- 1K<n<10K
tags:
- camouflaged-object-detection
- background-removal
- salient-object-detection
configs:
- config_name: default
  data_files:
  - path: data/test-*
    split: test
dataset_info:
  features:
  - dtype: image
    name: image
  - dtype: image
    name: mask
  splits:
  - name: test
    num_examples: 4121
---

# NC4K

The **NC4K** camouflaged-object test set — 4121 images with binary masks, as
one `test` split, which is how the benchmark is published and scored.

```python
from datasets import load_dataset

ds = load_dataset("nobg/NC4K", split="test")   # 4121 rows
ds[0]["image"]   # PIL, original resolution
ds[0]["mask"]    # PIL, the binary mask
```

## Why this mirror exists

NC4K is the largest camouflaged-object *test* set and is published **test-only**: every
paper that reports it reports it over all 4121 images. The upstream mirror
[`PassbyGrocer/NC4K`](https://huggingface.co/datasets/PassbyGrocer/NC4K) had split it
2884 / 618 / 619 into train / validation / test — a partition invented by the mirror, not
present in the benchmark. Scoring its `test` split yields a number over 619 images that
**no published result is comparable to**, while looking like a valid NC4K score.

This mirror concatenates the three back into one `test` split and drops the invented
boundary. Image and mask bytes are **bit-identical** to the source (verified by SHA-256 on
all 4121 rows of both columns, in order — nothing is decoded or re-encoded);
`gt` is renamed to `mask` for uniformity with the other `nobg` sets, and the per-object
`instance` column is dropped.

The source also ships a fourth, single-row `valid-00000-of-00001.parquet` that its own
dataset config does not reference. It is **not** a 4122th image: the row is
a 352×352 resize — the standard COD training resolution — whereas all 4121 real
rows are at original resolution across 1 878 distinct sizes, none of them 352×352. It is a
preprocessing artifact and is excluded.

## Licensing

**No license is declared** — not by NC4K's authors and not by the upstream mirror. It is
left unset here rather than guessed; check with the original authors before any use beyond
research. Same posture as [`nobg/COD10K`](https://huggingface.co/datasets/nobg/COD10K).

## Citation

```bibtex
@inproceedings{lv2021simultaneously,
  title={Simultaneously Localize, Segment and Rank the Camouflaged Objects},
  author={Lv, Yunqiu and Zhang, Jing and Dai, Yuchao and Li, Aixuan and Liu, Bowen and Barnes, Nick and Fan, Deng-Ping},
  booktitle={CVPR},
  year={2021}
}
```