In the past half-century, with the development of computer technology, neural networks have been widely used in many fields such as images, speeches, and decision-making. To improve the accuracy of neural networks, different scholars have designed a large number of network structures, and thus neural networks have become more and more complex and multi-parametric. As a result, the training process of neural networks has strong non-convexity, and different initial parameters of the same network often train different models. To more accurately describe the performance of two networks, predecessors proposed to evaluate the distribution of the performance of different random seeds on different models trained by the same network through the statistical method of stochastic dominance. On this basis, this study believes that the distribution of the performance of different models on different samples in a test set is also worthy of attention, and thus the stochastic dominance method is applied to compare the distribution of the performance of different models on different samples. Through the experiments on the networks applied in image segmentation, this study finds that for the two models trained by different networks, although one of them has certain advantages in the performance score, it may show stronger dispersion on different samples in the test set. The practical application, however, requires the neural network model with a better performance score and dispersion as small as possible. The stochastic dominance method can effectively compare different models for the selection of a more suitable one.