title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑÑ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð½Ð° Repka Pi
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: Development and optimization of a machine learning model with the capability to run on Repka Pi
Creators: name: ÐÐ°Ð»ÐµÑÐ¸Ð½, ÐÐ»ÐµÐºÑÐµÐ¹
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: Ð¾ÑÑÐ»ÐµÐ¶Ð¸Ð²Ð°Ð½Ð¸Ðµ Ð»ÑÐ´ÐµÐ¹
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
YOLO
DeepSORT
MIAP
Repka Pi
people tracking
deep learning

description: Ð ÑÐ°Ð¼ÐºÐ°Ñ Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð° ÑÐ¸ÑÑÐµÐ¼Ð° Ð¾ÑÑÐ»ÐµÐ¶Ð¸Ð²Ð°Ð½Ð¸Ñ Ð»ÑÐ´ÐµÐ¹ Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐµ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² YOLOv8n Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° DeepSORT Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ OSNet, Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð½Ð° Ð´Ð°ÑÐ°ÑÐµÑÐµ market1501. ÐÐ°ÑÐ°ÑÐµÑ MIAP Ð±ÑÐ» Ð²ÑÑÑÐ½ÑÑ Ð¾ÑÐ¸ÑÐµÐ½ Ð¾Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, Ð½Ðµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ñ Ð¿Ð¾Ð´ ÐºÑÐ¸ÑÐµÑÐ¸Ð¸ ÑÐµÑÐ°ÐµÐ¼Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸. ÐÑÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ PyTorch, Ultralytics, DVC. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÐºÐ²Ð°Ð½ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¸ Ð¿ÑÑÐ½Ð¸Ð½Ð³. ÐÐ¾Ð´ÐµÐ»Ñ Ð±ÑÐ»Ð° Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¿Ð¾Ð´ ÑÐ°ÐºÐ¸Ðµ ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ¸ ÐºÐ°Ðº ONNX Runtime, OpenCV, NCNN. Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐµÑ 0.35 mAP@0.5-0.95. Ð¡ÐºÐ¾ÑÐ¾ÑÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐµÑ 177 ÐºÐ°Ð´ÑÐ° Ð² ÑÐµÐºÑÐ½Ð´Ñ Ð½Ð° ÑÐµÐ»ÐµÐ²Ð¾Ð¼ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ðµ. Ð Ð°Ð±Ð¾ÑÐ¾ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÑÐµÐ½Ð° Ð½Ð° Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»ÑÐ½Ð¾Ð¼ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐµ, Ð¸Ð·-Ð·Ð° Ð½ÐµÑÐ¾Ð²Ð¼ÐµÑÑÐ¸Ð¼Ð¾ÑÑÐ¸ ÑÐ°ÑÑÐ¸ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼Ð¾Ð¹ Repka Pi Ð¾Ð¿ÐµÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ð¾Ð¹ Repka OS. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ¶Ð´Ð°ÑÑ ÐºÐ¾ÑÑÐµÐºÑÐ½Ð¾ÑÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ¸ÑÑÐµÐ¼Ñ.
description: As part of the thesis, a system for tracking people in video streams was built based on the YOLOv8n object detection model and the DeepSORT algorithm using the OSNet convolutional neural network, pre-trained on the market1501 dataset. The MIAP dataset was manually cleaned of images that did not meet the task criteria. PyTorch, Ultralytics, and DVC libraries were used for training. Quantization and pruning were used as optimization methods. The model was optimized for frameworks such as ONNX Runtime, OpenCV, and NCNN. The accuracy of the trained object detection model is 0.35 mAP@0.5-0.95. The detection model operates at a speed of 177 frames per second on the target device. The systems functionality was tested on a personal computer due to the incompatibility of some libraries with the Repka OS operating system used by Repka Pi. The testing results confirm the functionality of the implemented system.