From the course: Deep Learning: Image Recognition

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Extracting features with a pre-trained neural network

Extracting features with a pre-trained neural network - Python Tutorial

From the course: Deep Learning: Image Recognition

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Extracting features with a pre-trained neural network

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- [Narrator] Let's use transfer learning to build an image recognition system that can identify pictures of dogs. The first step is to build a feature extractor that can extract training features from our images. Let's get started. First, we need some training data. I've included some along with the example code. Let's take a look here in the training data folder. First, I have a sub-folder called dogs. These pictures are 64 by 64 pixel images from the image net dataset. If you're building your own image recognition system, you can use your own pictures of whatever kind of objects you wanna recognize instead. Next, we have a folder called "not dogs." These are various pictures of anything that's not a dog. It's important that these pictures are as varied as possible, so that the model can learn the difference between dogs and other types of objects. Alright, let's take a look at the code. Open up "04_feature_extraction.py". We're gonna write the code that will use the pretrained model…

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