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