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Convolutional Neural Networks
Each iteration is now more expensive because we have more training data.
Therefore, let's run for 50 iterations only. We see that by doing this we reach
an accuracy of 85.91%:
Figure 16: Results after 50 iterations
Figure 17: Final accuracy of 85.91% displayed
The results obtained during our experiments are summarized in the following figure:
Figure 18: Accuracy on CIFAR-10 with different networks
A list of state-of-the-art results for CIFAR-10 is available online (http://rodrigob.
github.io/are_we_there_yet/build/classification_datasets_results.
html). As of April 2019, the best result has an accuracy of 96.53% [3].
Predicting with CIFAR-10
Let's suppose that we want to use the deep learning model we just trained for
CIFAR-10 for a bulk evaluation of images.
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