  One of the biggest challenges in machine learning is data collection.
Training data is an important part since it determines how the model will
behave. In object classification, capturing a large number of images per object
and in different conditions is not always possible and can be very
time-consuming and tedious. Accordingly, this work explores the creation of
artificial images using a game engine to cope with limited data in the training
dataset. We combine real and synthetic data to train the object classification
engine, a strategy that has shown to be beneficial to increase confidence in
the decisions made by the classifier, which is often critical in industrial
setups. To combine real and synthetic data, we first train the classifier on a
massive amount of synthetic data, and then we fine-tune it on real images.
Another important result is that the amount of real images needed for
fine-tuning is not very high, reaching top accuracy with just 12 or 24 images
per class. This substantially reduces the requirements of capturing a great
amount of real data.
