README.md

---
pretty_name: AQUABENCH
license: other
license_name: aquabench-mixed
language:
- en
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
tags:
- underwater
- marine
- benchmark
- vision-foundation-models
- semantic-segmentation
size_categories:
- 10K<n<100K
extra_gated_heading: "Access AQUABENCH"
extra_gated_prompt: >-
  AQUABENCH redistributes nine third-party underwater datasets. The data is NOT
  relicensed: every subset keeps its original license (see the table in this card
  and the dataset_license.txt in each subset folder). Some subsets are
  non-commercial (LIACi: CC BY-NC-SA 4.0; TrashCan: academic/personal use only,
  commercial use requires permission from JAMSTEC) and some are share-alike
  (COU, LIACi).
extra_gated_fields:
  Affiliation: text
  I agree to comply with the original license of every subset I use: checkbox
  I will cite the original dataset publications and AQUABENCH: checkbox
configs:
- config_name: coralscapes
  data_files:
  - split: train
    path: coralscapes/train/*.parquet
  - split: validation
    path: coralscapes/validation/*.parquet
  - split: test
    path: coralscapes/test/*.parquet
- config_name: cou
  data_files:
  - split: train
    path: cou/train/*.parquet
  - split: validation
    path: cou/validation/*.parquet
  - split: test
    path: cou/test/*.parquet
- config_name: deepfish
  data_files:
  - split: train
    path: deepfish/train/*.parquet
  - split: validation
    path: deepfish/validation/*.parquet
  - split: test
    path: deepfish/test/*.parquet
- config_name: l4s
  data_files:
  - split: train
    path: l4s/train/*.parquet
  - split: validation
    path: l4s/validation/*.parquet
  - split: test
    path: l4s/test/*.parquet
- config_name: liaci
  data_files:
  - split: train
    path: liaci/train/*.parquet
  - split: validation
    path: liaci/validation/*.parquet
  - split: test
    path: liaci/test/*.parquet
- config_name: seaclear
  data_files:
  - split: train
    path: seaclear/train/*.parquet
  - split: validation
    path: seaclear/validation/*.parquet
  - split: test
    path: seaclear/test/*.parquet
- config_name: suim
  data_files:
  - split: train
    path: suim/train/*.parquet
  - split: validation
    path: suim/validation/*.parquet
  - split: test
    path: suim/test/*.parquet
- config_name: trashcan
  data_files:
  - split: train
    path: trashcan/train/*.parquet
  - split: validation
    path: trashcan/validation/*.parquet
  - split: test
    path: trashcan/test/*.parquet
- config_name: uiis10k
  data_files:
  - split: train
    path: uiis10k/train/*.parquet
  - split: validation
    path: uiis10k/validation/*.parquet
  - split: test
    path: uiis10k/test/*.parquet
---

# AQUABENCH: Evaluating Vision Foundation Models for Underwater Segmentation

AQUABENCH is a unified **semantic segmentation** benchmark for evaluating how
well pretrained vision foundation models (VFMs) transfer to underwater imagery.
It repurposes nine publicly available underwater datasets into a common
image–mask interface with standardized splits, resolutions, and background
conventions, while **preserving each dataset's original class taxonomy**.

- **Paper:** *AQUABENCH: Evaluating Vision Foundation Models for Underwater
  Segmentation*, T. Globisch and S. Oehmcke, ECCV 2026 Workshops (Marine Vision)
- **Code (conversion pipeline + benchmark interface, MIT):** `<link to repository>`
- **Contact:** torben.globisch@uni-rostock.de, stefan.oehmcke@uni-rostock.de
  (University of Rostock)

## Subsets

Each dataset is a separate config. `#Cls` includes the background class (index 0).

| Config        | Dataset                                   | Domain                      | Train | Val   | Test  | #Cls | Size (H×W) | License               |
|---------------|-------------------------------------------|-----------------------------|------:|------:|------:|-----:|------------|-----------------------|
| `coralscapes` | Coralscapes                               | Coral reef health           | 1,517 |   166 |   392 |   39 | 512×1024   | Apache-2.0            |
| `cou`         | Common Objects Underwater (COU)           | Man-made objects, pool/lake/ocean | 6,753 | 1,952 | 958 | 24 | 576×1024 | CC BY-SA 4.0          |
| `deepfish`    | DeepFish                                  | Fish habitats               |   310 |   124 |   186 |    2 | 576×1024   | MIT                   |
| `l4s`         | Looking for Seagrass (L4S)                | Seagrass coverage           | 4,223 |   610 | 1,204 |    2 | 576×1024   | BSD-2-Clause          |
| `liaci`       | LIACi                                     | Ship hull inspection (ROV)  | 1,233 |   137 |   191 |   11 | 576×1024   | CC BY-NC-SA 4.0       |
| `seaclear`    | SeaClear                                  | Marine debris, shallow water| 6,071 |   674 | 1,865 |   41 | 576×1024   | CC BY 4.0             |
| `suim`        | SUIM                                      | Scene-level underwater      | 1,297 |   228 |   110 |    8 | 480×640    | MIT                   |
| `trashcan`    | TrashCan 1.0                              | Deep-sea debris             | 5,459 |   606 | 1,147 |   23 | 576×1024   | Non-commercial (JAMSTEC) |
| `uiis10k`     | UIIS10K                                   | Aggregated underwater scenes| 7,234 |   804 | 2,010 |   11 | 480×640    | Apache-2.0            |
| **Total**     |                                           |                             | **34,097** | **5,301** | **8,063** | | | |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("TorbenGl/AQUABENCH", "suim", split="train")
sample = ds[0]
image, mask = sample["image"], sample["mask"]   # RGB image, class-index mask
```

Per-config metadata (class vocabulary, ignore indices, split sizes) is in
`_metadata/<config>.json`.

## Data format

Every config stores rows of `(image, mask)` as PNG-compressed bytes in parquet
files, one file set per split.

- **Masks** are single-channel class-index maps. **Index 0 = background** in
  all configs. COCO datasets without an explicit background polygon get the
  background filled automatically.
- **Evaluation:** mIoU over all foreground classes, **excluding background**.

## Preprocessing

The same deterministic transformation applies to all models.

1. **Task conversion.** COCO datasets (`cou`, `liaci`, `seaclear`, `trashcan`,
   `uiis10k`) are flattened from RLE/polygons to per-pixel class masks. Where
   annotations overlap, the later-drawn label wins. For LIACi the draw order is
   set so that surface conditions (corrosion, paint peeling, defects)
   are drawn on top of structural classes. For all other COCO sets the raw
   order is kept, with <5% single-pair overwrite. Image+mask datasets are
   remapped from grayscale values (`l4s`, `deepfish`) or RGB colours (`suim`).
2. **Aspect-ratio normalization.** Each dataset is assigned to the family
   closest to its median aspect ratio: 16:9 → 576×1024, 2:1 → 512×1024 or
   4:3 → 480×640. All sizes are multiples of 16, giving exact patch-16 grids.
3. **Resize and crop.** Each image is resized to cover the target size while
   keeping its aspect ratio (bilinear for images, nearest-neighbour for masks),
   so nothing is stretched or padded. It is then cropped along the oversized
   axis at the position that keeps the most foreground pixels, and no
   annotated class is removed. Images without foreground are center-cropped.
4. **Splits.** Official splits are used where available. If only train/val
   exist, val becomes test. SeaClear has no predefined split, so train and test
   are partitioned at the **dive level** to avoid leakage between highly
   correlated frames. `liaci`, `seaclear`, `trashcan` and `uiis10k` get a
   stratified 10% validation holdout carved from train, stratified by class
   presence with seed 42. The validation split is used only for monitoring,
   not for model selection.

Excluded variants: the material and supplementary labels of SeaClear, the
material variant of TrashCan, the original-label variant of Coralscapes,
CoralMask, and USOD10K.

## Known overlaps

Duplicates were detected with perceptual hashing (dHash, Hamming distance ≤ 4).
UIIS10K shares about 1,533 near-duplicate images with SUIM and about 165 with
TrashCan, so the subsets are not fully independent. See the paper appendix
for the ranking sensitivity analysis.

## License

**AQUABENCH does not relicense the data.** Each subset keeps its original
ownership, copyright and license; see `<config>/dataset_license.txt`.

- Using the **full benchmark** means complying with **all** nine licenses.
- Using a **subset** means complying only with the licenses of that subset.
- **Non-commercial:** `liaci` (CC BY-NC-SA 4.0) and `trashcan` (academic and
  personal use only). Commercial use of TrashCan requires prior permission from
  JAMSTEC, and attribution to JAMSTEC J-EDI is required in all cases.
- **Share-alike:** derivatives of `cou` (CC BY-SA 4.0) and `liaci`
  (CC BY-NC-SA 4.0) must be released under the same license.
- `uiis10k` contains images that originate from SUIM and TrashCan. The terms
  of those upstream sources may also apply to them.
- The AQUABENCH conversion pipeline and benchmark interface are released under
  the MIT License, which does not cover the data.

All rights in the constituent datasets remain with their respective rights
holders.

## Citation

If you use AQUABENCH, cite the benchmark **and** the original publication of
every subset you use.

```bibtex
@inproceedings{globisch2026aquabench,
  title     = {{AQUABENCH}: Evaluating Vision Foundation Models for Underwater Segmentation},
  author    = {Globisch, Torben and Oehmcke, Stefan},
  booktitle = {European Conference on Computer Vision (ECCV) Workshops},
  year      = {2026},
  note      = {To appear}
}

@inproceedings{sauder2025coralscapes,
  title     = {The Coralscapes Dataset: Semantic Scene Understanding in Coral Reefs},
  author    = {Sauder, Jonathan and Domazetoski, Viktor and Banc-Prandi, Guilhem and Perna, Gabriela and Meibom, Anders and Tuia, Devis},
  booktitle = {ICCV},
  pages     = {2115--2122},
  year      = {2025}
}

@inproceedings{mukherjee2025cou,
  title     = {The Common Objects Underwater ({COU}) Dataset for Robust Underwater Object Detection},
  author    = {Mukherjee, Rishi and Singh, Sakshi and McWilliams, Jack and Sattar, Junaed},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages     = {18597--18603},
  year      = {2025}
}

@article{saleh2020deepfish,
  title   = {A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis},
  author  = {Saleh, Alzayat and Laradji, Issam H. and Konovalov, Dmitry A. and Bradley, Michael and Vazquez, David and Sheaves, Marcus},
  journal = {Scientific Reports},
  volume  = {10},
  number  = {1},
  pages   = {14671},
  year    = {2020},
  doi     = {10.1038/s41598-020-71639-x}
}

@inproceedings{reus2018seagrass,
  title     = {Looking for Seagrass: Deep Learning for Visual Coverage Estimation},
  author    = {Reus, Gereon and M{\"o}ller, Thomas and J{\"a}ger, Jonas and Schultz, Stewart T. and Kruschel, Claudia and Hasenauer, Julian and Wolff, Viviane and Fricke-Neuderth, Klaus},
  booktitle = {2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)},
  pages     = {1--6},
  year      = {2018},
  doi       = {10.1109/OCEANSKOBE.2018.8559302}
}

@article{waszak2023liaci,
  title   = {Semantic Segmentation in Underwater Ship Inspections: Benchmark and Data Set},
  author  = {Waszak, Maryna and Cardaillac, Alexandre and Elves{\ae}ter, Brian and R{\o}d{\o}len, Frode and Ludvigsen, Martin},
  journal = {IEEE Journal of Oceanic Engineering},
  volume  = {48},
  number  = {2},
  pages   = {462--473},
  year    = {2023},
  doi     = {10.1109/JOE.2022.3219129}
}

@article{duras2024seaclear,
  title   = {A dataset for detection and segmentation of underwater marine debris in shallow waters},
  author  = {{\DJ}ura{\v{s}}, An{\dj}ela and Wolf, Ben J. and Ilioudi, Athanasios and Palunko, Ivana and De Schutter, Bart},
  journal = {Scientific Data},
  volume  = {11},
  number  = {1},
  pages   = {921},
  year    = {2024},
  doi     = {10.1038/s41597-024-03759-2}
}

@inproceedings{islam2020suim,
  title     = {Semantic Segmentation of Underwater Imagery: Dataset and Benchmark},
  author    = {Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz, Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages     = {1769--1776},
  year      = {2020}
}

@article{hong2020trashcan,
  title   = {TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris},
  author  = {Hong, Jungseok and Fulton, Michael and Sattar, Junaed},
  journal = {arXiv preprint arXiv:2007.08097},
  year    = {2020}
}

@article{li2025uiis10k,
  title   = {Advancing Marine Research: {UWSAM} Framework and {UIIS10K} Dataset for Precise Underwater Instance Segmentation},
  author  = {Li, Hua and Lian, Shijie and Li, Zhiyuan and Cong, Runmin and Li, Chongyi and Yang, Laurence T. and Zhang, Weidong and Kwong, Sam},
  journal = {arXiv preprint arXiv:2505.15581},
  year    = {2025}
}
```