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.
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 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.
Citation
@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}
}