| ---
|
| 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}
|
| }
|
| ```
|
|
|
