Research on the Construction of an Efficient and Lightweight Online Detection Method for Tiny Surface Defects through Model Compression and Knowledge Distillation
In response to the current issues of poor real-time performance, high computational costs, and excessive memory usage of object detection algorithms based on deep convolutional neural networks in embedded devices, a method for improving deep convolutional neural networks based on model compression a...
Main Authors: | Qipeng Chen, Qiaoqiao Xiong, Haisong Huang, Saihong Tang, Zhenghong Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-01-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/13/2/253 |
Similar Items
-
Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation
by: Chen, Qipeng, et al.
Published: (2024) -
FasterAI: A Lightweight Library for Neural Networks Compression
by: Nathan Hubens, et al.
Published: (2022-11-01) -
Efficient and Controllable Model Compression through Sequential Knowledge Distillation and Pruning
by: Leila Malihi, et al.
Published: (2023-09-01) -
Progressive multi-level distillation learning for pruning network
by: Ruiqing Wang, et al.
Published: (2023-04-01) -
Model Compression Algorithm via Reinforcement Learning and Knowledge Distillation
by: Botao Liu, et al.
Published: (2023-11-01)