title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð±Ð¸Ð½Ð°ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ 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: Application of binary neural networks for image recognition
Creators: name: ÐÐ¾ÑÐ¾Ð»ÐµÐ², ÐÐµÐ½Ð¸Ñ
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: 2022
Subjects: ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð±Ð¸Ð½Ð°ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
python
pytorch
convolutional neural networks
binary neural networks
image detection

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð±Ð¸Ð½Ð°ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð»Ð¸Ñ. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð±Ð¸Ð½Ð°ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð»Ð¸Ñ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Pyton Ð¸ ÑÑÐµÐ¹Ð¼Ð¾ÑÐºÐ° Pytorch ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð° Ð±Ð¸Ð½Ð°ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ¾Ð¹ SSD. ÐÐ±ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¸ ÑÐ°Ð·Ð²ÐµÑÐ½ÑÑÐ° Ð½Ð° Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ðµ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð¾ Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐ¾Ð¼ MediaTek Helio P22 MT6762 (4 x Cortex-A53 2000 ÐÐÑ, 4 x Cortex-A53 1500 ÐÐÑ). ÐÐ»Ñ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ°ÑÐ¸Ð¸ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð¾ Android Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ Ð¿Ð¾ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð»Ð¸Ñ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¸ Ñ ÐºÐ°Ð¼ÐµÑÑ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ð³Ð¾ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð°. Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÐµÑÑÑ Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿Ð¾Ð»Ð½Ð¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ (Ð±ÐµÐ· Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð±Ð¸Ð½Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸) Ð½Ð° Ð¿ÑÐµÐ´Ð¼ÐµÑ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ, Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÐºÐ°Ð´ÑÐ° Ð¸ ÑÐ°Ð·Ð¼ÐµÑÐ° Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð±Ð¸Ð½Ð°ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÑ TinyML, Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÑÐµÐ¹Ð¼Ð¾ÑÐºÐ° Tensorflow Lite, Ð¸ Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÐ¼Ð¸, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÐ¼Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Bi-Real Net Ð¸ ABC-Net.
description: This work is devoted to the study of the use of binary neural networks to solve the problem of face detection. During the study, an analysis of methods for constructing binary neural networks, neural network architectures and datasets for face detection is carried out.Based on the Pyton programming language and the Pytorch framework, a binary neural network with SSD architecture was implemented and trained. The trained model was optimized and deployed on a mobile device with a MediaTek Helio P22 MT6762 processor (4 x Cortex-A53 2000 MHz, 4 x Cortex-A53 1500 MHz). An Android application for face detection in the frame from the device's camera was created and used for model demonstration and performance testing. The resulting model was compared with a full-precision neural network model (without binarization) in terms of recognition accuracy, recognition time per frame, and size of model. The implemented binary model is also compared with TinyML model optimized using Tensorflow Lite framework and with models implemented based on the Bi-Real Net and ABC-Net methods.