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 ASL Alphabet Images dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The data set is a collection of alphabets from the American Sign Language, separated into 29 folders representing the various classes. The training data set contains 87,000 images which are 200×200 pixels. There are 29 classes, of which 26 are for the letters A-Z and three labels for SPACE, DELETE, and NOTHING. The test data set contains only 28 images to encourage the use of real-world test images.
In this Take4 iteration, we will construct a CNN model based on the ResNet152V2 architecture to predict the ASL alphabet letters based on the available images.
ANALYSIS: In this Take4 iteration, the ResNet152V2 model’s performance achieved an accuracy score of 99.83% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 95.71%. Finally, the final model processed the test dataset with an accuracy score of 100%.
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: Kaggle ASL Alphabet Images
Dataset ML Model: Multi-class image classification with numerical attributes
Dataset Reference: https://www.kaggle.com/grassknoted/asl-alphabet
One potential source of performance benchmarks: https://www.kaggle.com/grassknoted/asl-alphabet/code
The HTML formatted report can be found here on GitHub.