A Hybrid Image Training Dataset of 12 Freshwater Ornamental Fish Species

This dataset was created to train a fish recognition model for 12 common freshwater aquarium species. The data was gathered using three distinct methods. First, existing public datasets were downloaded and combined from online sources like Kaggle. Second, a Python web-scraping script and manual downloads from Google Images were used to collect additional pictures. The third method was primary self-collection, conducted specifically for the Cherry Barb class. To gather this data, a Sony ZV-1F camera and a smartphone were used to photograph live Cherry Barbs in a home planted aquarium, which introduces real-world visual challenges like water refraction and plant occlusions. The dataset is organized into three folders to enable easy reuse: "Original Cropped Images" contains centered specimens ready for classification models, "Raw Aquarium Shots" holds the full, unedited tank photos of the Cherry Barbs for object detection tasks, and "Augmented Images" provides an artificially expanded dataset for deep learning. Researchers can use this hybrid data to train computer vision models and test how well they perform on clear web-sourced images compared to real-world aquarium photos.