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 Lemon Quality Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The dataset contains 2,533 images of lemons on concrete surfaces. The research team collected these images to investigate the possibilities of enforcing a fruit quality control system. The images can be broken down into three different labels: good quality, bad quality, and empty background.
ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 98.22% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 99.01%.
CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.
Dataset ML Model: Multi-Class classification with numerical features
Dataset Used: Lemon Quality Dataset
Dataset Reference: https://www.kaggle.com/datasets/yusufemir/lemon-quality-dataset
One source of potential performance benchmarks: https://www.kaggle.com/datasets/yusufemir/lemon-quality-dataset/code
The HTML formatted report can be found here on GitHub.