Fish-Pak: Fish species dataset from Pakistan for visual features based classification.

Area of Science:

• Ichthyology
• Computer Vision
• Machine Learning

Background:

• Fishes represent the most diverse vertebrate group, with over 33,000 species.
• Identifying fish species visually is challenging for the public, especially from market samples.
• Accurate fish classification relies heavily on high-quality image datasets for training machine learning models.

Purpose of the Study:

• To introduce Fish-Pak, a novel image dataset for fish species classification.
• To provide a standardized dataset for evaluating computer vision and machine learning algorithms in ichthyology.
• To facilitate research on the impact of dataset quality and classifier parameters on fish recognition performance.

Main Methods:

• The study presents Fish-Pak, an image dataset comprising 6 distinct fish species.
• Images were captured using a single camera under consistent conditions near Head Qadirabad, Chenab River, Pakistan.
• The dataset includes species such as Grass carp, Common carp, Mori, Rohu, Silver carp, and Thala.

Main Results:

• Fish-Pak contains images of six economically important fish species.
• The dataset is designed to enable comparative analysis of various classifier parameters, like learning rate and momentum.
• The consistent capture environment ensures fairness for evaluating classification algorithms.

Conclusions:

• Fish-Pak serves as a valuable resource for advancing fish species identification through machine learning.
• The dataset supports the development and benchmarking of convolutional neural network (CNN) models for visual feature-based classification.
• This initiative by the University of Gujrat aims to bridge computer science and zoology for practical applications in fisheries and biodiversity monitoring.