Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Large Scale Fish Images dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains nine different seafood types collected from a supermarket in Izmir, Turkey, for a university-industry collaboration project at Izmir University of Economics, and this work was published in ASYU 2020. For each class, there are 1000 augmented images and their pair-wise augmented ground truths.

In iteration Take1, we constructed a CNN model based on the InceptionV3 architecture to predict the leaf’s health state based on the available images.

In this Take2 iteration, we will construct a CNN model based on the DenseNet201 architecture to predict the leaf’s health state based on the available images.

ANALYSIS: In iteration Take1, the InceptionV3 model’s performance achieved an accuracy score of 99.65% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy score of 93.83%.

In this Take2 iteration, the DenseNet201 model’s performance achieved an accuracy score of 99.79% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy score of 97.56%.

CONCLUSION: In this iteration, the DenseNet201-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: A Large-Scale Dataset for Fish Segmentation and Classification

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: Ulucan, Oguzhan and Karakaya, Diclehan and Turkan, Mehmet (2020), “A Large-Scale Dataset for Fish Segmentation and Classification,” 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE (https://ieeexplore.ieee.org/abstract/document/9259867)

One potential source of performance benchmarks: https://www.kaggle.com/crowww/a-large-scale-fish-dataset

The HTML formatted report can be found here on GitHub.

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