Data · dataset · 2026
Oreochromis niloticus Fingerlings WhiteTray Dataset (20 Fish per Image Polygon annotation)
Listed in Teesside University Research Data Repository
The Oreochromis niloticus Fingerlings WhiteTray Dataset is a curated repository of annotated images designed for the development and benchmarking of Artificial Intelligence models in precision aquaculture, particularly for fish detection and counting tasks.
Description
The dataset contains images of Nile tilapia (Oreochromis niloticus) fingerlings captured in a controlled environment using white trays (white tray setup), which enhances visual contrast and facilitates object detection and segmentation processes.
Each image contains exactly 20 fish, ensuring a fixed and controlled density across the entire dataset, annotation method Polygon (Segments). This characteristic makes the dataset especially suitable for evaluating counting algorithms, density estimation methods, and object detection models under standardized conditions. The images were obtained under consistent lighting and background conditions, minimizing environmental variability while still preserving common computer vision challenges such as: Variations in orientation and positioning Subtle scale differences among fingerlings Visual similarity between instances (low inter-class variance) All images are annotated with bounding boxes in YOLO format, supporting direct use in state-of-the-art object detection frameworks.
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This dataset is highly recommended for training and evaluating deep learning architectures, including the YOLO family (v8 to v11) and Faster R-CNN, particularly in scenarios requiring precise counting and detection under controlled densities. Folder structure: ON_Fingerlings_WhiteTray_20_polygon_fish.zip/ ├── data.yaml ├── train/ │ ├── images/ (157 jpg files) │ └── labels/ (157 txt files) ├── valid/ │ ├── images/ (20 jpg files) │ └── labels/ (20 txt files) └── test/ ├── images/ (19 jpg files) └── labels/ (19 txt files)
Links
Where it is published
- DOI doi.org/10.17632/jpmp539j5t.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Aquaculture · Artificial intelligence · Computer Science & AI · Computer vision · Earth & Environmental Science
- Inferred from text
- Image 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/jpmp539j5t.1 | 15 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:300501 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Aquaculture'] |
| concepts[field].anzsrc:field:460304 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Computer Vision'] |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |