From f04e074544e6e067f268b887a2245d6154c27808 Mon Sep 17 00:00:00 2001 From: sumn2u Date: Thu, 21 Dec 2023 05:49:59 -0600 Subject: [PATCH] fix the broken image reference --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index f2b167d..cd9f184 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,9 +37,9 @@ Integration of machine learning models with mobile devices presents a promising The app uses the [TrashNet](https://github.com/garythung/trashnet) dataset to recognize different types of waste materials including plastic, glass, paper, and organic waste with various machine learning models, including InceptionV3 [@feng_office_2020], MobileNetV2 [@yong_application_2023], InceptionResnet V2 [@lee_novel_2021], ResNet [@girsang_convolutional_2022], MobileNet[@nurahmadan_mobile_2021], and Xception[@rismiyati_xception_2020]. -The app also provides information on how to dispose of the waste and what recycling options are available. This approach can be customized to meet the user's specific needs, including local waste management regulations and individual household waste disposal habits and preferences. The home screen of the app is show in \autoref{fig:deep-waste-app} +The app also provides information on how to dispose of the waste and what recycling options are available. This approach can be customized to meet the user's specific needs, including local waste management regulations and individual household waste disposal habits and preferences. The home screen of the app is show in \autoref{fig:deep_waste_app} -![Deep Waste App\label{fig:deep_waste_app}](deep-waste-app.png){width="100%"} +![Deep Waste App Home Screen\label{fig:deep_waste_app}](deep-waste-app.png){width="100%"} The classification models are then converted into a lite format, such as [TFLite](https://www.tensorflow.org/lite/guide), which enables them to be used on mobile devices with limited resources. This format allows for fast loading times, smaller size, and compatibility with various programming languages and platforms. \autoref{fig:deep_waste_app_workflow} describes the overall workflow of the app.