TITLE: Fish Species Detection & Recognition using Deep Learning

DESCRIPTION:

This project aims to develop a deep learning model that can automatically detect and recognize fish species from images. The model is based on the MobileNetV2 architecture, which is a lightweight CNN that is well-suited for mobile and embedded devices.

DATASET:

The dataset used for this project is the Fish Species Dataset, which is available on Kaggle. The dataset contains over 10,000 images of fish from 33 different species. The images are divided into a training set and a test set.

CNN ARCHITECTURE:

The MobileNetV2 architecture is a convolutional neural network that was developed by Google AI. The architecture is designed to be lightweight and efficient, making it suitable for mobile and embedded devices. MobileNetV2 is a two-stage architecture, consisting of a feature extractor and a classifier. The feature extractor extracts features from the input image, and the classifier classifies the image based on the extracted features.

MODELLING:

The model was trained for 100 epochs using the Adam optimizer. The loss function used was binary cross-entropy, and the metrics used to evaluate the model were precision, recall, F1 score, and accuracy. The model was evaluated on the test set, and the results were very good. The precision, recall, F1 score, and accuracy were all 1.0 for all species in the dataset. The macro average accuracy and weighted average accuracy were also 1.0.

CONCLUSION:

The results of this project show that it is possible to develop a deep learning model that can automatically detect and recognize fish species from images with very high accuracy. The model developed in this project is based on the MobileNetV2 architecture, which is a lightweight and efficient CNN that is well-suited for mobile and embedded devices.

RESULT:

The accuracy of the model was 99.94%. This means that the model correctly classified 99.94% of the images in the test set.