The explosive growth of computation and energy cost of artificial
intelligence has spurred strong interests in new computing modalities as
potential alternatives to conventional electronic processors. Photonic
processors that execute operations using photons instead of electrons, have
promised to enable optical neural networks with ultra-low latency and power
consumption. However, existing optical neural networks, limited by the
underlying network designs, have achieved image recognition accuracy much lower
than state-of-the-art electronic neural networks. In this work, we close this
gap by introducing a large-kernel spatially-varying convolutional neural
network learned via low-dimensional reparameterization techniques. We
experimentally instantiate the network with a flat meta-optical system that
encompasses an array of nanophotonic structures designed to induce
angle-dependent responses. Combined with an extremely lightweight electronic
backend with approximately 2K parameters we demonstrate a nanophotonic neural
network reaches 73.80\% blind test classification accuracy on CIFAR-10 dataset,
and, as such, the first time, an optical neural network outperforms the first
modern digital neural network -- AlexNet (72.64\%) with 57M parameters,
bringing optical neural network into modern deep learning era