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 Shoes Footwear Types Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The dataset contains over 13,000 images of shoes in five classes. The training set contains 2,000 training images assigned to each class. The validation set contains 500 validation images assigned to each class. The test set has a random number of images assigned to each class.
ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 84.64% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 82.72%.
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: Shoes Classification Dataset
Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/shoes-classification-dataset-13k-images
One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/shoes-classification-dataset-13k-images/code
The HTML formatted report can be found here on GitHub.