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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.
INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.
ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 98.47% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.00%.
CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.
Dataset ML Model: Binary classification with numerical features
Dataset Used: Car vs. Bike Classification Dataset
Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset
One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code
The HTML formatted report can be found here on GitHub.