Ornamental fish dataset from an underwater environment
In underwater environments, there are more than 33,000 species of fish, which are identified by different visual characteristics, such as the shape, color, and shape of the head. These characteristics are difficult to identify for ordinary people, therefore scientists and aquaculturists are using photographic and video cameras as tools to quantify the species and identify the state and shape. Providing these tools with computer vision algorithms and deep learning techniques, since recorded images of fish can be time-consuming and expensive to process and analyze manually, it is an interesting problem for researchers. However, the use of these techniques depends on the visual characteristics extracted by the set of images with which it has been trained. This article presents an image data set of 3 different fish species, Goldenfish (Carassius auratus), Molly (Poecilia sphenops), and Zebra (Danio rerio), obtaining a different number of images per species. The data were obtained by means of a camera in a natural environment where the species, housed in the environment protected by the aquaculturist.
Steps to reproduce
1) Load the dataset by using scientific software like Matlab or Python. 2) Do preprocessing. 3) Train a machine learning model. 4) Test and validate the model.