How This Deep Learning Image Classification Demo Works

This MNIST image classification demo trains a Convolutional Neural Network (CNN) directly in your browser. Specifically, it utilizes high-performance JavaScript to process the famous MNIST digits dataset. Because the dataset is relatively straightforward, you should expect to achieve roughly 99% accuracy within just a few minutes.

Furthermore, this setup demonstrates that you do not always need massive server power to run deep learning models. By running the code locally, we eliminate the need for complex server configurations. Consequently, this makes the technology accessible to anyone with a web browser.

Technical Architecture and Data Augmentation

The network takes a standard 28×28 MNIST image as its primary input. However, it crops a random 24×24 window before the training phase begins. In the field of machine learning, this specific technique is called data augmentation. Therefore, this process improves how the model learns and prevents it from simply memorizing data.

To perform an accurate prediction, the system samples four different random crops. In addition, it averages the probabilities across all these crops to produce a final result. On a standard machine, this network runs at approximately 5ms per pass. As a result, the performance remains efficient enough for real-time interaction and immediate visual feedback.

Optimization in the MNIST Image Classification Demo

By default, this MNIST image classification demo utilizes Adadelta. This is a robust, per-parameter adaptive method for choosing step sizes. As a result, you do not have to worry about manually changing learning rates during training. This automation makes the process much more friendly for beginners.

Nevertheless, we have included custom text fields for manual adjustments. This allows advanced users to play around with the SGD and Momentum settings. Ultimately, experimenting with these options helps you understand how different math algorithms impact speed and accuracy. Moreover, you can see these changes happen live as the agent learns.