---
license: cc-by-4.0
task_categories:
- image-classification
language:
- en
tags:
- biology
- marine
- fish
- reef
- underwater
- inaturalist
pretty_name: Tropical Reef Fish Dataset
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': acanthurus_coeruleus
          '1': naso_unicornis
          '2': scarus_ghobban
          '3': thalassoma_lunare
          '4': amphiprion_ocellaris
          '5': chromis_viridis
          '6': chaetodon_lunula
          '7': pomacanthus_imperator
          '8': rhinecanthus_aculeatus
          '9': pterois_volitans
          '10': arothron_meleagris
          '11': carcharhinus_melanopterus
          '12': unknown_fish
          '13': no_fish
  splits:
  - name: train
    num_bytes: 8950108178
    num_examples: 7794
  download_size: 8517861535
  dataset_size: 8950108178
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Tropical Reef Fish Dataset

A dataset of tropical reef fish images for species-level image classification, with additional negative classes for out-of-distribution rejection.

## Dataset Description

Images were scraped from iNaturalist using research-grade observations with CC0 or CC-BY licenses. The dataset covers 12 species across 9 families, targeting fish commonly encountered by snorkelers at depths of 0–10 meters in tropical reef environments.

Two negative classes are included to enable the model to reject inputs that don't match any known species: `unknown_fish` for fish outside the 12 target species, and `no_fish` for non-fish objects.

This dataset was created as part of a bachelor thesis at ZHAW School of Engineering (Institute of Embedded Systems, InES) with the goal of training a lightweight image classifier for fully offline deployment on mobile devices.

## Classes

### Pinned Species (12 classes)

| Scientific Name             | Common Name            | Family         | Images |
| --------------------------- | ---------------------- | -------------- | ------ |
| _Acanthurus coeruleus_      | Atlantic Blue Tang     | Acanthuridae   | 500    |
| _Naso unicornis_            | Bluespine Unicornfish  | Acanthuridae   | 500    |
| _Scarus ghobban_            | Blue-barred Parrotfish | Labridae       | 500    |
| _Thalassoma lunare_         | Moon Wrasse            | Labridae       | 500    |
| _Amphiprion ocellaris_      | Ocellaris Anemonefish  | Pomacentridae  | 376    |
| _Chromis viridis_           | Blue-green Chromis     | Pomacentridae  | 425    |
| _Chaetodon lunula_          | Raccoon Butterflyfish  | Chaetodontidae | 500    |
| _Pomacanthus imperator_     | Emperor Angelfish      | Pomacanthidae  | 310    |
| _Rhinecanthus aculeatus_    | Lagoon Triggerfish     | Balistidae     | 500    |
| _Pterois volitans_          | Red Lionfish           | Scorpaenidae   | 500    |
| _Arothron meleagris_        | Guineafowl Puffer      | Tetraodontidae | 500    |
| _Carcharhinus melanopterus_ | Blacktip Reef Shark    | Carcharhinidae | 483    |

**Total pinned species: 5,594 images**

### Negative Classes (2 classes)

| Class          | Source      | Description                                             | Images |
| -------------- | ----------- | ------------------------------------------------------- | ------ |
| `no_fish`      | Open Images | Non-fish objects (24 object categories, 50 images each) | 1,200  |
| `unknown_fish` | iNaturalist | Fish species outside the 12 target species              | 1,000  |

**Total negative: 2,200 images**

**Grand total: 7,794 images across 14 classes**

## No-Fish Categories

The `no_fish` class contains 50 images each from the following Open Images categories:

- **Urban / everyday**: Chair, Car, Table, Person, Dog, Cat, Bicycle, Bus, Truck, Motorcycle, Airplane, Boat, House, Bottle
- **Underwater / coastal**: Sea turtle, Tortoise, Starfish, Jellyfish, Crab, Lobster, Dolphin, Whale
- **Natural**: Mushroom, Tree

## Data Collection

- **Pinned species & unknown fish**: [iNaturalist](https://www.inaturalist.org) API (research-grade observations only), CC0 and CC-BY licenses validated at the individual photo level
- **No fish**: [Open Images V6](https://storage.googleapis.com/openimages/web/index.html) via the `openimages` Python package
- **Image resolution**: Original resolution as provided by the respective source
- **Target depth range**: 0–10m (snorkeling depth)

## Structure

```

data/
<family_scientific_name>/
<species_scientific_name>/
<species_scientific_name>\_0000.jpg
...
unknown_fish/
unknown_fish_0000.jpg
...
no_fish/
chair1.jpg
...
dataset.json

```

## Metadata

`dataset.json` contains four top-level arrays:

### `labels`

Species metadata for the 12 pinned classes:

- `species_taxon_id` / `family_taxon_id` — iNaturalist taxon IDs
- `family_common_name` / `family_scientific_name`
- `species_common_name` / `species_scientific_name`
- `image_count`

### `negative_labels`

- `label_id` — either `unknown_fish` or `no_fish`
- `image_count`

### `data`

Per-image records for pinned species:

- `species_taxon_id` — links to `labels` array
- `file` — relative path to image
- `observation_id` / `observation_uri` — original iNaturalist observation
- `photo_id` / `photo_url_original` — original photo reference
- `photo_license` — `cc0` or `cc-by`
- `photo_attribution` — attribution string as required by license

### `negative_data`

Per-image records for negative classes, with a consistent structure:

- `label_id` — `unknown_fish` or `no_fish`
- `file` — relative path to image
- `source` — `inaturalist` or `openimages`
- `meta` — source-specific metadata:
  - For `unknown_fish`: `photo_id`, `observation_id`, `observation_uri`, `photo_url_original`, `photo_license`, `photo_attribution`, `taxon_id`, `taxon_scientific_name`
  - For `no_fish`: `original_label` (the Open Images category name)

## Intended Use

This dataset is intended for training lightweight convolutional neural networks for species-level fish classification on mobile devices, with built-in support for out-of-distribution rejection. It is not exhaustive of all tropical reef fish species and is not intended for scientific surveys or biodiversity assessments.

## Limitations

- Class imbalance among pinned species (310–500 images per species)
- No bounding box annotations — classification labels only
- Image quality varies (motion blur, partial occlusion, waterproof housing distortion)
- `unknown_fish` images are drawn from the same iNaturalist distribution as pinned species and may not represent all possible out-of-distribution fish inputs

## Attribution

Images from iNaturalist are contributed by observers under CC0 and CC-BY licenses. Full per-image attribution is available in `dataset.json` under `negative_data[*].meta.photo_attribution` and `data[*].photo_attribution`. Please credit original observers when redistributing or publishing work derived from this dataset.

Images from Open Images are subject to their respective licenses as documented by Google.

## License

Dataset metadata and structure: [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)

Individual images: see `photo_license` in `dataset.json` (iNaturalist images: CC0 or CC-BY; Open Images: per-image license as documented by Google)