In our case, because we restrict ourselves to only 8% of the dataset, the problem is much harder. In the resulting competition, top entrants were able to score over 98% accuracy by using modern deep learning techniques. The current literature suggests machine classifiers can score above 80% accuracy on this task. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. "In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. dogs competition (with 25,000 training images in total), a bit over two years ago, it came with the following statement: How difficult is this problem? When Kaggle started the cats vs. Being able to make the most out of very little data is a key skill of a competent data scientist. So this is a challenging machine learning problem, but it is also a realistic one: in a lot of real-world use cases, even small-scale data collection can be extremely expensive or sometimes near-impossible (e.g. That is very few examples to learn from, for a classification problem that is far from simple. We also use 400 additional samples from each class as validation data, to evaluate our models. In our examples we will use two sets of pictures, which we got from Kaggle: 1000 cats and 1000 dogs (although the original dataset had 12,500 cats and 12,500 dogs, we just took the first 1000 images for each class). ![]() 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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