  Picture based vehicle protection handling is a significant region with
enormous degree for mechanization. In this paper we consider the issue of
vehicle harm characterization, where a portion of the classifications can be
fine-granular. We investigate profound learning based procedures for this
reason. At first, we attempt legitimately preparing a CNN. In any case, because
of little arrangement of marked information, it doesn't function admirably. At
that point, we investigate the impact of space explicit pre-preparing followed
by tweaking. At last, we explore different avenues regarding move learning and
outfit learning. Trial results show that move learning works superior to space
explicit tweaking. We accomplish precision of 89.5% with blend of move and
gathering learning.
