Lambari 4cm Imagery: A Custom Dataset of 4 cm Fingerlings for Deep Learning Training

The Lambari 4 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 Lambari (Astyanax altiparanae) at the 4 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 positioning.Subtle scale differences among the 4 cm specimens.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 and specific growth stages. dataset/ ├── data.yaml ├── train/ │ ├── images/ (553 jpg files) │ └── labels/ (553 txt files | 3,468 bounding boxes) ├── valid/ │ ├── images/ (42 jpg files) │ └── labels/ (42 txt files | 313 bounding boxes) └── test/ ├── images/ (42 jpg files) └── labels/ (42 txt files | 227 bounding boxes)