How your computer-vision model learned to stop worrying and love the Python
Developers can create large datasets of synthetic videos to train computer-vision models using software written by a team of researchers led by Google. Data is one of the most important ingredients of deep learning. You need good quality samples in large quantities to boost a model’s performance, though datasets of this ilk are by their nature few and far between. If you need images or videos, you could scrape them from internet profile pages and galleries, though this is tedious to clean and process at large scale, and can give everyone privacy and legal headaches. If you need more complex data, such as video footage with depth and object annotations, your available sources dwindle further. Fake computer-generated material avoids these problems by giving you exactly what you need, and there are various ways to output it, including by using machine learning. And yes, there is the caveat that you need to know what you’re doing when training a system on synthetic data: if the dataset doesn’t match reality, your model is going to get a skewed view of the world.
