Datasets:
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license: cc-by-4.0
task_categories:
- object-detection
- image-to-text
- zero-shot-object-detection
language:
- en
pretty_name: ORCA 100-image random sample
size_categories:
- n<1K
source_datasets:
- WongYukKwan/ORCA
tags:
- marine-species
- coco-format
---
# ORCA 100-image random sample
A random sample of **100 images** (with their annotations) drawn from the ORCA dataset
([WongYukKwan/ORCA](https://huggingface.co/datasets/WongYukKwan/ORCA)), the benchmark from
*ORCA: Object Recognition and Comprehension for Archiving Marine Species* (WACV 2026,
[arXiv:2512.21150](https://arxiv.org/abs/2512.21150)).
## How it was sampled
- 100 images sampled uniformly at random with a fixed seed (`random.Random(42)`) from the
14,645 images in the source dataset.
- The 100 sampled images span all 670 species categories in expectation; the sample contains
269 bounding-box annotations (with captions).
- Annotations in `data.json` are filtered to only the sampled images; the full `categories`
taxonomy (670 entries) is kept unchanged so category IDs still resolve.
## Structure
COCO format, identical to the source dataset:
- `data.json` — `{"images": [...], "annotations": [...], "categories": [...]}`
- `images/` — the 100 sampled image files
Each annotation carries `bbox`, `caption`, and `label`
(0 = LLM-generated positive caption, 1 = LLM-generated negative caption,
2 = expert-refined positive caption).
## License
The source dataset is released under **CC-BY-4.0**; this sample inherits that license.
Please cite the original ORCA paper if you use this sample:
```bibtex
@InProceedings{Wong_2026_WACV,
author = {Wong, Yuk-Kwan and Liang, Haixin and Ma, Zeyu and Chen, Yiwei and Zheng, Ziqiang and Gotama, Rinaldi and Sebastian, Pascal and Sparks, Lauren D. and Yeung, Sai-Kit},
title = {ORCA: Object Recognition and Comprehension for Archiving Marine Species},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2026},
pages = {1597-1609}
}
```
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