Image classification using transfer learning implemented using PyTorch

American Sign Language Image Obtained from the US National Institute on Deafness and Other Communication Disorders

Earlier I created a simple image classification model using logistic regression on the American Sign Language MNIST dataset. The dataset consists of multiple grayscale 28x28 image pixels. You can read more about it here. The accuracy of the model was just a little bit above 50%. The accuracy could be improved drastically just by adding multiple layers and non-linear activation functions like the Rectified Linear Unit (ReLU). Another approach could be to use a simple Convolutional Neural Network (CNN). Since the images were grayscale, it is fairly easier to construct a model for that.

This time, I will be constructing…

A simple image classification script using PyTorch

American Sign Language Alphabet

For starters, PyTorch is an open source machine learning framework that can be used in both Python and Java/C++. It is commonly used for deep learning and natural language processing. For more information about its installation, documentation, and tutorials about using PyTorch you may accessed it here.

Another term mentioned in the title is Logistic Regression. Logistic Regression is a type of analysis that is used if the goal is to determine the category/class of the output. For example, if the email is a spam or not. …

Gryan Galario

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