  Hamiltonian parameter estimation is crucial in condensed matter physics, but
time and cost consuming in terms of resources used. With advances in
observation techniques, high-resolution images with more detailed information
are obtained, which can serve as an input to machine learning (ML) algorithms
to extract Hamiltonian parameters. However, the number of labeled images is
rather limited. Here, we provide a protocol for Hamiltonian parameter
estimation based on a machine learning architecture, which is trained on a
small amount of simulated images and applied to experimental spin configuration
images. Sliding windows on the input images enlarges the number of training
images; therefore we can train well a neural network on a small dataset of
simulated images which are generated adaptively using the same external
conditions such as temperature and magnetic field as the experiment. The neural
network is applied to the experimental image and estimates magnetic parameters
efficiently. We demonstrate the success of the estimation by reproducing the
same configuration from simulation and predict a hysteresis loop accurately.
Our approach paves a way to a stable and general parameter estimation.
