![]() ![]() To acquire a few hundreds or thousands of training images belonging to the classes you are interested in, one possibility would be to use the Flickr API to download pictures matching a given tag, under a friendly license. jpg images:ĭata/ train/ dogs/ dog001.jpg dog002.jpg. a training data directory and validation data directory containing one subdirectory per image class, filled with.If you have a NVIDIA GPU that you can use (and cuDNN installed), that's great, but since we are working with few images that isn't strictly necessary. a machine with Keras, SciPy, PIL installed. ![]() Our setup: only 2000 training examples (1000 per class) ImageDataGenerator for real-time data augmentation.fit_generator for training Keras a model using Python data generators.This will lead us to cover the following Keras features: fine-tuning the top layers of a pre-trained network.using the bottleneck features of a pre-trained network.training a small network from scratch (as a baseline).In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples -just a few hundred or thousand pictures from each class you want to be able to recognize. Please seeįor an up-to-date alternative, or check out chapter 8 of my book "Deep Learning with Python (2nd edition)". Note: this post was originally written in June 2016.
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