Accuracy of Deep Learning Models Over the Years

Over the years, there were many achievements in deep learning, many of which were directly related to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC, or ImageNet challenge for short). We talked about some of those milestones in deep learning in the past and how their unique innovations have helped shape the deep learning landscape today.

Today let us look at how the accuracy of these significant models has increased over the years.

Deep Learning Models Over the Years

When reporting the accuracy of classification models two accuracy measures are typically used: Top-1 Accuracy, and Top-5 Accuracy.

• Top-1 Accuracy - Where the highest probability/confidence prediction from the model matches the expected class
• Top-5 Accuracy - Where the expected class is within the top 5 predictions of the model

Using Data Augmentations in Keras

When I did the article on Using Bottleneck Features for Multi-Class Classification in Keras and TensorFlow, a few of you asked about using data augmentation in the model. So, I decided to do few articles experimenting various data augmentations on a bottleneck model. As a start, here's a quick tutorial explaining what data augmentation is, and how to do it in Keras.

The idea of augmenting the data is simple: we perform random transformations and normalization on the input data so that the model we’re training never sees the same input twice. With little data, this can greatly reduce the chance of the model overfitting.

But, trying to manually add transformations to the input data would be a tedious task.

Which is why Keras has built-in functions to do just that.

The Keras Preprocessing package has the ImageDataGeneraor function, which can be configured to perform the random transformations and the normalization of input images as needed. And, coupled with the flow() and flow_from_directory() functions, can be used to automatically load the data, apply the augmentations, and feed into the model.

Let’s write a small script to see the data augmentation capabilities of ImageDataGeneraor.

OpenAI and the Dota 2 Bot

OpenAI – a non-profit AI research company, founded by Elon Musk and Sam Altman, which focuses on developing friendly AI – unveiled their Dota 2 AI Bot in August 2017, which is capable of defeating top Dota professional players in 1v1 matches.

OpenAI Logo

Dota 2 is a multiplayer online battle arena (MOBA) game developed by the Valve Corporation. First released on July 2013, the game is a sequel to the community game Defence of the Ancients (DotA) which was released back in 2003 as a mod for the game Warcraft III.

The Dota 2 Game Logo

A typical match of Dota 2 is played by five-verses-five (5v5), although other variations of the game exists, such as 1v1. The players each choose a ‘hero’ from 115 playable characters, each with its strengths and weaknesses, and various abilities and powers. The game is played in a real-time strategy manner, where each ream battles the other and attempts to destroy the ‘Ancient’ (the large structure on their base) of the opposing team while defending their own.