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Paper Abstract and Keywords
Presentation 2024-03-15 15:50
Towarods Similarity-Preserving Post-Training Quantization for Lightweight Maize Disease Detection
Carlos Victorino Padeiro, Tse-Wei Chen, Takahiro Komamizu, Ichiro IDE (Nagoya University) IMQ2023-90 IE2023-145 MVE2023-119
Abstract (in Japanese) (See Japanese page) 
(in English) The traditional crop disease diagnosis process, relying on expert visual observation, is costly, time-consuming, and prone to human error. Though Convolutional Neural Network (CNN) offers promising alternatives, it suffers from high resource demands, making them inaccessible to farmers. Lightweight models that can work on resource-restricted devices without network access are needed to address this issue. In this perspective, we propose a solution that leverages Post-Training Quantization (PTQ) to convert high-precision models into lower-precision ones while preserving similar features. While quantization is a promising method for building lightweight CNN models for crop disease detection, the quantized models’ quality needs to be improved. To overcome this problem, we propose a PTQ method called Similarity-Preserving Quantization (SPQ), which ensures equivalent activation patterns for similar crop images in both original and quantized models. Experimental evaluation applying SPQ to MobileNetV2 showed that the proposed method improved about 30% of throughput while keeping detection performance
Keyword (in Japanese) (See Japanese page) 
(in English) Quantization / Post-Training Quantization (PTQ) / Similarity-Preserving (SP) / Crop Diseasse / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 432, IE2023-145, pp. 414-419, March 2024.
Paper # IE2023-145 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IMQ2023-90 IE2023-145 MVE2023-119

Conference Information
Committee IE MVE CQ IMQ  
Conference Date 2024-03-13 - 2024-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Sangyo Shien Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） 
Paper Information
Registration To IE 
Conference Code 2024-03-IE-MVE-CQ-IMQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Towarods Similarity-Preserving Post-Training Quantization for Lightweight Maize Disease Detection 
Sub Title (in English)  
Keyword(1) Quantization  
Keyword(2) Post-Training Quantization (PTQ)  
Keyword(3) Similarity-Preserving (SP)  
Keyword(4) Crop Diseasse  
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1st Author's Name Carlos Victorino Padeiro  
1st Author's Affiliation Nagoya University (Nagoya University)
2nd Author's Name Tse-Wei Chen  
2nd Author's Affiliation Nagoya University (Nagoya University)
3rd Author's Name Takahiro Komamizu  
3rd Author's Affiliation Nagoya University (Nagoya University)
4th Author's Name Ichiro IDE  
4th Author's Affiliation Nagoya University (Nagoya University)
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Speaker Author-1 
Date Time 2024-03-15 15:50:00 
Presentation Time 20 minutes 
Registration for IE 
Paper # IMQ2023-90, IE2023-145, MVE2023-119 
Volume (vol) vol.123 
Number (no) no.430(IMQ), no.432(IE), no.433(MVE) 
Page pp.414-419 
#Pages 6 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 

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