ГЛУБОКАЯ НЕЙРОННАЯ СЕТЬ ДЛЯ ОБНАРУЖЕНИЯ ТОЧЕК НА ИЗОБРАЖЕНИЯХ С ИСПОЛЬЗОВАНИЕМ KERAS

Abstract

Convolutional neural networks are a powerful tool for image processing and analysis. They allow you to automatically extract features from the input data and apply them 
 to classification, object detection and other computer vision tasks. This article presents the convolutional neural networks architecture developed using the Keras library. The architecture of a convolutional neural network, which consists of several successive layers, is analyzed. The structure of the model has been built, which will be trained using the Adam optimizer and monitor the recall and precision metrics during the training process. The results of experiments are presented, which showed that the trained model successfully detects points in images, achieving high accuracy and completeness. The proposed model can be used in various areas where the detection of facial points in photographs is required.