Efficiency improvement analysis of mask r-cnn

Neural networks are a large number of interconnected mathematical neural models. Neural networks are very widely used to solve a variety of problems: speech synthesis and recognition problems, computer vision problems, and others. Convolutional neural networks can be applied to solve problems of object localization and segmentation. Solutions to these problems are widely applied in the following areas: autonomous car safety (accurate real-time detection of pedestrians, obstacles, road signs), mechanics and robotics (machine monitoring and control, real-time identification of defective products, etc.), disease detection (cancer cell detection in photos, etc.), transportation (route planning, finding the shortest route, etc.) and others. The Mask-RCNN network is used to address these issues. Mask R-CNN is a deep convolutional neural network used to solve the problem of instance segmentation. Neural networks have many parameters, some of which are parameters that the network learns during learning, which are called model parameters, and others that do not change during learning, called hyperparameters. Certain combinations of hyperparameters allow for better network estimates in less time. Optimization algorithms can be used to discover these combinations. Several hyperparameter optimization algorithms that can be used are: manual search, random search, grid search, Bayesian optimization algorithm. The Bayesian optimization used tries to discover the global optimum in the smallest number of steps, so it is used to discover the optimal combinations of hyperparameters in the Mask R-CNN network to get the highest possible network estimate