Opto-electronic neural networks integrate optical front-ends with electronic back-ends to enable fast and energy-efficient vision. However, conventional end-to-end optimization of both the optical and electronic modules is limited by costly simulations and large parameter spaces. We introduce a two-stage strategy for designing opto-electronic convolutional neural networks (CNNs): first, train a standard electronic CNN, then realize the optical front-end implemented as a metasurface array through direct kernel optimization of its first convolutional layer. This approach reduces computational and memory demands by hundreds of times and improves training stability compared to end-to-end optimization. On monocular depth estimation, the proposed two-stage design achieves twice the accuracy of end-to-end training under the same training time and resource constraints.