  Multi-component modelling of galaxies is a valuable tool in the effort to
quantitatively understand galaxy evolution, yet the use of the technique is
plagued by issues of convergence, model selection and parameter degeneracies.
These issues limit its application over large samples to the simplest models,
with complex models being applied only to very small samples. We attempt to
resolve this dilemma of "quantity or quality" by developing a novel framework,
built inside the Zooniverse citizen science platform, to enable the
crowdsourcing of model creation for Sloan Digitial Sky Survey galaxies. We have
applied the method, including a final algorithmic optimisation step, on a test
sample of 198 galaxies, and examine the robustness of this new method. We also
compare it to automated fitting pipelines, demonstrating that it is possible to
consistently recover accurate models that either show good agreement with, or
improve on, prior work. We conclude that citizen science is a promising
technique for modelling images of complex galaxies, and release our catalogue
of models.
