Oreochromis niloticus Fingerlings WhiteTray Dataset (30 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 30 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_30_box_fish.zip/ ├── data.yaml ├── train/ │ ├── images/ (157 jpg files) │ └── labels/ (157 txt files) ├── valid/ │ ├── images/ (19 jpg files) │ └── labels/ (19 txt files) └── test/ ├── images/ (20 jpg files) └── labels/ (20 txt files)