Oreochromis niloticus Fingerlings WhiteTray Dataset (20 Fish per Image Box 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 Box. 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_box_fish.zip/ ├── data.yaml ├── train/ │ ├── images/ (159 jpg files) │ └── labels/ (159 txt files) ├── valid/ │ ├── images/ (20 jpg files) │ └── labels/ (20 txt files) └── test/ ├── images/ (20 jpg files) └── labels/ (20 txt files)尼罗罗非鱼幼鱼白盘数据集（Oreochromis niloticus Fingerlings WhiteTray Dataset）是一套经精心整理的标注图像库，专为精准水产养殖领域的人工智能模型开发与基准测试打造，尤其适用于鱼类检测与计数任务。 该数据集包含在可控环境下采用白盘搭建场景拍摄的尼罗罗非鱼（Oreochromis niloticus）幼鱼图像，白盘可提升视觉对比度，助力目标检测与分割任务的顺利开展。 每张图像恰好包含20尾幼鱼，确保整个数据集内的养殖密度固定且可控，标注方法采用边界框（Box）。该特性使得本数据集尤其适合在标准化条件下评估计数算法、密度估计方法与目标检测模型。 所有图像均在一致的光照与背景条件下采集，尽可能降低环境变量带来的干扰，同时仍保留了计算机视觉领域常见的挑战，包括：幼鱼朝向与位置存在差异、幼鱼间存在细微体型尺度差异、个体间视觉相似度较高（类间方差较低）。 所有图像均采用YOLO格式的边界框进行标注，可直接适配当前顶尖的目标检测框架。 本数据集高度适用于训练与评估深度学习架构，包括YOLO系列（v8至v11）与Faster R-CNN，尤其适用于需要在可控密度下实现精准计数与检测的应用场景。 数据集文件夹结构如下： ON_Fingerlings_WhiteTray_20_box_fish.zip/ ├── data.yaml ├── train/ │ ├── images/（共159个jpg文件） │ └── labels/（共159个txt文件） ├── valid/ │ ├── images/（共20个jpg文件） │ └── labels/（共20个txt文件） └── test/ ├── images/（共20个jpg文件） └── labels/（共20个txt文件）