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Synthetic Data Generation for Computer Vision in Blender
(part 1)
What: This entry gives an introduction to synthetic-data-generation, and how you can use it via Blender to train performant and robust vision models. We’ll provide an overview of the Blender setup and, for demonstrative purposes, present a concrete visual classification scenario from the fashion domain.
Why: to leverage Blender procedural capabilities and adopt a data-centric approach to get better machine-learning models with little or no need for human annotations.
Who: we will rely on Blender >3.1 and Python ≥ 3.7. Generated images can then be used for any downstream task, regardless of possible dependent frameworks (e.g. Tensorflow, PyTorch).
Synthetic Data Generation (SDG)
Synthetic Data Generation (SDG) encompasses a variety of methods that aim at programmatically generating data to support downstream tasks. In statistics and Machine Learning (ML), the resulting goal is to synthesize samples with the same distribution of a target domain, to be used for model training or testing purposes. It is part of the data-centric ML approach, where in order to achieve better performances we actively work on data, instead of models, algorithms, or architectures.
