Image Counts For Neural Network Training

Pete Warden shares his rule of thumb for how many images you need to train a neural network:

In the early days I would reply with the technically most correct, but also useless answer of “it depends”, but over the last couple of years I’ve realized that just having a very approximate rule of thumb is useful, so here it is for posterity:

You need 1,000 representative images for each class.

Like all models, this rule is wrong but sometimes useful. In the rest of this post I’ll cover where it came from, why it’s wrong, and what it’s still good for.

Read on to learn where the number 1000 came from and get some good hints, like flipping and rescaling images.

Related Posts

Power BI AutoML

Teo Lachev takes a look at AutoML in Power BI: Let’s see how AutoML works based on what’s in the private preview (the usual disclaimer is that things will probably change). To start with, AutoML requires a dataflow (a note to Microsoft here is that AutoML will become more pervasive if it’s available in Power […]

Read More

Using Convolutional Neural Networks To Recognize Features In Images

Michael Grogan shows how you can use Keras to perform image recognition with a convolutional neural network: VGG16 is a built-in neural network in Keras that is pre-trained for image recognition. Technically, it is possible to gather training and test data independently to build the classifier. However, this would necessitate at least 1,000 images, with […]

Read More

Categories

December 2017
MTWTFSS
« Nov Jan »
 123
45678910
11121314151617
18192021222324
25262728293031