  Quantum sensors leverage matter's quantum properties to enable measurements
with unprecedented spatial and spectral resolution. Among these sensors, those
utilizing nitrogen-vacancy (NV) centers in diamond offer the distinct advantage
of operating at room temperature. Nevertheless, signals received from NV
centers are often complex, making interpretation challenging. This is
especially relevant in low magnetic field scenarios, where standard
approximations for modeling the system fail. Additionally, NV signals feature a
prominent noise component. In this Letter, we present a signal-to-image deep
learning model capable of automatically inferring the number of nuclear spins
surrounding a NV sensor and the hyperfine couplings between the sensor and the
nuclear spins. Our model is trained to operate effectively across various
magnetic field scenarios, requires no prior knowledge of the involved nuclei,
and is designed to handle noisy signals, leading to fast characterization of
nuclear environments in real experimental conditions. With detailed numerical
simulations, we test the performance of our model in scenarios involving
varying numbers of nuclei, achieving an average error of less than $2\
\rm{kHz}$ in the estimated hyperfine constants.
