title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð±Ð¸Ð½Ð°ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research on the effectiveness of the use of binary neural networks for image classification
Creators: name: Ð¡ÑÐ´Ð°ÐºÐ¾Ð², ÐÐ²Ð³ÐµÐ½Ð¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð±Ð¸Ð½Ð°ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Python
Pytorch
neural networks
convolutional neural networks
binary neural networks
image classification

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ, Ð½Ð°ÑÐºÐ¾Ð»ÑÐºÐ¾ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð±Ð¸Ð½Ð°ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² Ð·Ð°Ð´Ð°ÑÐµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð² Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð±Ð¸Ð½Ð°ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð¸Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ. ÐÑÐ»Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ ÐºÐ¾Ð½ÐºÑÑÐµÐ½ÑÐ½ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ð±Ð¸Ð½Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÐ¾ÑÐ»Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð±ÑÐ» Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐ¹ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð¸ÑÐµÐ»Ñ Ð´Ð»Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸. ÐÑÐ»Ð¸ Ð¸Ð·ÑÑÐµÐ½Ñ Ð´Ð°ÑÐ°ÑÐµÑÑ Ð´Ð»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¸ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð½Ð¾Ð²ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð´Ð»Ñ ÑÐ¿ÑÐ¾ÑÐµÐ½Ð¸Ñ Ð¿ÐµÑÐµÐ²Ð¾Ð´Ð° Ð¿Ð¾Ð»Ð½Ð¾ÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð² Ð±Ð¸Ð½Ð°ÑÐ½ÑÑ Ñ Ð·Ð°Ð´Ð°Ð½Ð½Ð¾Ð¹ ÐºÐ¾Ð½ÑÐ¸Ð³ÑÑÐ°ÑÐ¸ÐµÐ¹, Ð±ÑÐ»Ð° Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð° Ð¿Ð¾Ð»Ð½Ð¾ÑÐ¾ÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð°, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²Ð»ÐµÐ½Ð½ÑÑ Ð±Ð¸Ð½Ð°ÑÐ¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ¾Ð², Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð´Ð»Ñ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð¾ÑÑÐ°Ð»ÑÐ½ÑÐ¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸, Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ» ÑÐºÐ¾Ð½ÑÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½ ÑÐ²Ð¾Ð¹ Ð´Ð°ÑÐ°ÑÐµÑ Ñ Ð¼ÐµÑÐ°Ð½Ð¸ÑÐµÑÐºÐ¸Ð¼Ð¸ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÐ¼Ð¸ ÑÑÐµÐ´ÑÑÐ²Ð°Ð¼Ð¸, Ð² Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ Ð»ÐµÐ¶Ð¸Ñ ImageNet. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ¾ÑÐ½Ð¾Ð¹ (Ð±ÐµÐ· Ð±Ð¸Ð½Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸) Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ñ ÐµÑ Ð±Ð¸Ð½Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¼Ð¸ Ð²Ð°ÑÐ¸Ð°Ð½ÑÐ°Ð¼Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸. Ð¡ÑÐ°Ð²Ð½Ð¸Ð²Ð°ÑÑÑÑ ÑÐ°ÐºÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ ÐºÐ°Ðº: Bi-Real Net, Melius Net, AdaBin. ÐÑÑÐ»ÐµÐ´ÑÑÑÑÑ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹: ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ, ÑÐ°Ð·Ð¼ÐµÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸, ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð¾Ð¿ÐµÑÐ°ÑÐ¸Ð¹ Ð¿ÑÐ¸ Ð¸Ð½ÑÐµÑÐµÐ½ÑÐµ.
description: This paper examines how effective the use of binary neural networks is in the problem of image classification. In the course of the work, the analysis of methods for building binary neural networks and their architectures was carried out. Competitive approaches to binarization of neural networks have been implemented. After the analysis, the most effective representative for the task was determined. Datasets for the image classification task were studied and a comparative analysis was performed. In the course of the work, a new method was developed to simplify the translation of a full-fledged neural network into a binary one with a given configuration, a fullfledged neural network model was transformed using the obtained method, as well as prepared binarized components, and characteristics were obtained for comparison with other methods, a comparative analysis was carried out. It also designed its own dataset with mechanical vehicles, which is based on ImageNet. The paper compares a full-fledged (without binarization) neural network model with its binarized implementation options. Methods such as: Bi-Real Net, Melius Net, AdaBin are compared. The characteristics of the models are investigated: recognition accuracy, model size, and the number of inference operations.