In this section, we will cover some more advanced options in the image classifier toolkit that let you do more.
The image classifier toolkit is based on a technique known as transfer learning. At a high level, model creation is accomplished by simply removing the output layer of the Deep Neural Network for 1000 categories, and taking the signals that would have been propagating to the output layer and feeding them as features to any classifier for our task.
The advanced options let you select from a set of pre-trained models which can result in a model having various size, performance, and accuracy characteristics.
Using the following option, you can change to use squeezenet which can trade off some accuracy for a smaller model with a lower memory and disk foot-print.
model = tc.image_classifier.create( train_data, target='label', model='squeezenet_v1.1')
GPUs can make creating an image classifier model much faster. If you have macOS 10.13 or higher, Turi Create will automatically use the GPU. If your Linux machine has an NVIDIA GPU, you can setup Turi Create to use the GPU, see instructions.
turicreate.config.set_num_gpus function allows you to control if GPUs are used:
# Use all GPUs (default) turicreate.config.set_num_gpus(-1) # Use only 1 GPU turicreate.config.set_num_gpus(1) # Use CPU turicreate.config.set_num_gpus(0)