  We present a new suite of over 1,500 cosmological N-body simulations with
varied Warm Dark Matter (WDM) models ranging from 2.5 to 30 keV. We use these
simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle
masses from images of DM field data. Our fiducial setup can make accurate
predictions of the WDM particle mass up to 7.5 keV at a 95% confidence level
from small maps that cover an area of (25 h$^{-1}$ Mpc)$^2$. We vary the image
resolution, simulation resolution, redshift, and cosmology of our fiducial
setup to better understand how our model is making predictions. Using these
variations, we find that our models are most dependent on simulation
resolution, minimally dependent on image resolution, not systematically
dependent on redshift, and robust to varied cosmologies. We also find that an
important feature to distinguish between WDM models is present with a linear
size between 100 and 200 h$^{-1}$ kpc. We compare our fiducial model to one
trained on the power spectrum alone and find that our field-level model can
make 2x more precise predictions and can make accurate predictions to 2x as
massive WDM particle masses when used on the same data. Overall, we find that
the field-level data can be used to accurately differentiate between WDM models
and contain more information than is captured by the power spectrum. This
technique can be extended to more complex DM models and opens up new
opportunities to explore alternative DM models in a cosmological environment.
