Oreochromis niloticus Fingerlings WhiteTray Dataset (20 Fish per Image Polygon annotation)

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.

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.

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)

尼罗罗非鱼幼鱼白盘数据集（Oreochromis niloticus Fingerlings WhiteTray Dataset）是一套经筛选整理的标注图像库，旨在开发与评测精准水产养殖领域的人工智能模型，尤其适用于鱼类检测与计数任务。

本数据集包含在可控环境下采用白盘装置（white tray setup）拍摄的尼罗罗非鱼（Oreochromis niloticus）幼鱼图像，该装置可提升视觉对比度，助力目标检测与分割任务的开展。

每张图像恰好包含20尾幼鱼，确保全数据集的养殖密度固定可控；所有标注均采用多边形（Polygon，Segments）形式。这一特性使得本数据集尤其适用于在标准化条件下评测计数算法、密度估计方法与目标检测模型。

所有图像均在光照与背景条件一致的环境下采集，尽可能降低环境变量带来的影响，但仍保留了计算机视觉任务中常见的挑战，包括：
- 个体的朝向与位置差异
- 幼鱼间存在细微的尺度差异
- 个体间视觉相似度高（类间方差较低）

所有图像均附带YOLO格式的边界框标注，可直接用于当前主流的目标检测框架。

本数据集被广泛推荐用于训练与评测深度学习架构，包括YOLO系列（v8至v11）与Faster R-CNN，尤其适用于需要在可控养殖密度下实现精准计数与检测的场景。

文件夹结构：
ON_Fingerlings_WhiteTray_20_polygon_fish.zip/
├── data.yaml
├── train/
│ ├── images/（含157个jpg文件）
│ └── labels/（含157个txt文件）
├── valid/
│ ├── images/（含20个jpg文件）
│ └── labels/（含20个txt文件）
└── test/
├── images/（含19个jpg文件）
└── labels/（含19个txt文件）