Multi-Class Image Classification Deep Learning Model for Fruits-360 Images Using TensorFlow Take 1

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 Fruits-360 Images dataset is a multi-class classification situation where we attempt to predict one of several (for this dataset 131) possible outcomes.

INTRODUCTION: This dataset contains 131 fruits and vegetables with 67692 training images and 22688 test images. All images have a resolution of 100 X 100 X 3 colors in the JPEG format. Each dataset includes 131 subdirectories, one for each type of fruit or vegetable.

In this Take1 iteration, we will construct a CNN model based on the InceptionV3 architecture to predict the fruit or vegetable name for the image.

ANALYSIS: In this Take1 iteration, the InceptionV3 model’s performance achieved an accuracy score of 99.46% after five epochs using the validation dataset. The same model processed the test dataset with an accuracy score of 98.03%.

CONCLUSION: In this iteration, the InceptionV3-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Fruits-360: A dataset of images containing fruits and vegetables.

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

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.