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 American Sign Language Alphabet 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 22,000 images of alphabets from American Sign Language, separated into 29 folders that represent the various classes. The research team collected these images to investigate the possibilities of reducing the communication gap between sign-language users and non-Sign language users.
ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 99.01% after three epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 88.47%.
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: American Sign Language Alphabet Dataset
Dataset Reference: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset
One source of potential performance benchmarks: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset/code
The HTML formatted report can be found here on GitHub.