Binary Class Image Classification Deep Learning Model for Meat Quality Assessment Using TensorFlow Take 2

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 Meat Quality Assessment dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The research team collected the dataset to develop a meat quality assessment system based on deep learning. The published paper explains all of the experimental results, proving the usability of the dataset and model. This dataset contains fresh and spoiled red meat samples from a supermarket in Izmir, Turkey, for a university-industry collaboration project at the Izmir University of Economics.

For this modeling project, we will predict whether an image represents a fresh or spoiled meat case. In this iteration, we will construct a CNN model based on the ResNet152V2 architecture to make predictions.

ANALYSIS: In this iteration, the ResNet152V2 model’s performance achieved an accuracy score of 98.88% after 15 epochs using the training dataset. The same model processed the validation dataset with an accuracy rate of 88.10%.

CONCLUSION: In this iteration, the ResNet152V2-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Meat Quality Assessment

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.