Oreochromis niloticus Imagery: A Custom Dataset of 8 cm Fingerlings for Deep Learning Training

The Oreochromis niloticus 8 cm 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 annotated using the Bounding Box method. The dataset contains images of Nile tilapia (Oreochromis niloticus) at the 8 cm growth stage captured in a controlled environment, which enhances visual contrast and facilitates object detection and segmentation processes. 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 positioningSubtle scale differences among the 8 cm specimensVisual 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 and specific growth stages. ON_8cm_WhiteTray_box_fish.zip/ ├── data.yaml ├── train/ │ ├── images/ (569 jpg files) │ └── labels/ (569 txt files | 4,072 bounding boxes) ├── valid/ │ ├── images/ (43 jpg files) │ └── labels/ (43 txt files | 320 bounding boxes) └── test/ ├── images/ (43 jpg files) └── labels/ (43 txt files | 316 bounding boxes)