Sea Animal Classification Using Deep Learning and CNN

Abstract

Artificial intelligence developments can substantially help with this difficult challenge. of classifying different species of sea animals based on their visual characteristics. With the use of convolutional neural networks (CNNs), this study seeks to create an accurate and effective method for classifying marine animals. The project adopts a methodical approach, starting with data collection and preprocessing, building a CNN architecture, training and tuning the model, assessing its performance, and deploying it for real-time predictions. The task at hand is gathering a varied array of of photos of marine animals and preparing them by shrinking and leveling the pixels values. Convolutional, pooling, and fully linked layers contribute to the architecture of the CNN model. The dataset is used to train the model, and the hyperparameters are tuned for the best results. The project’s completion highlights the accomplishment of its goals, including the creation of a reliable CNN-based model. for classifying sea animals. The trained model has remarkable accuracy when identifying identifying and categorizing different species of sea animals. The model’s performance will be further enhanced, its capabilities will be increased, and it will support marine re- search, conservation activities, and educational programs. Overall, this experiment demonstrates the effectiveness of CNNs in classifying sea animals and emphasizes the possibility for further development in the area. This research helps to understand and preserve marine ecosystems, and supports several scientific, conservation, and educational initiatives by precisely identifying and classifying sea animal species.