[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-189595-105":53,"doc-detail-189595-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","mobilenet-loss-comparison","MobileNet Loss Comparison","","This document presents a comparison of loss metrics across different training epochs for the MobileNet model, alongside other benchmark models like ResNet50 and VGG16. The data indicates that MobileNet achieves exceptionally high training and validation accuracy (99.97% and 99.94% respectively) with very low training and validation loss (0.0043). The accompanying graph visually demonstrates the convergence of training and validation loss curves for MobileNet across 100, 200, 500, and 1000 epochs, illustrating a stable and efficient learning process where the loss decreases significantly in the initial epochs and then plateaus, with minimal gap between training and validation loss, suggesting effective generalization. The comparison with ResNet50 and VGG16, which also show high accuracy and low loss, highlights MobileNet's competitive performance, particularly its efficiency for mobile and edge devices.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/mobilenet-loss-comparison/189595/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/mobilenet-loss-comparison/189595.png","ImageObject",442,249,{"name":88,"@type":89},"Skyler","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-23","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the training accuracy of MobileNet compared to ResNet50 and VGG16?","Question",{"text":108,"@type":109},"MobileNet achieves a training accuracy of 99.97%, which is higher than ResNet50 (97.61%) and VGG16 (98.31%).","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the validation loss of MobileNet compare across different epochs?",{"text":113,"@type":109},"The validation loss for MobileNet across 100, 200, 500, and 1000 epochs shows a decreasing trend initially, then stabilizes around 0.2-0.3, indicating consistent performance and generalization ability.",{"name":115,"@type":106,"acceptedAnswer":116},"What is the primary focus of the comparison presented in the document?",{"text":117,"@type":109},"The document focuses on comparing the training and validation loss of the MobileNet model across various training epochs, also benchmarking it against ResNet50 and VGG16.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},189595,1790000903,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":20,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":47},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","|  | 1 |  |\n| --- | --- | --- |\n\n\n|  | 2 |  |\n| --- | --- | --- |\n\n|  | 3 |  |\n| --- | --- | --- |\n\n|  | 4 |  |\n| --- | --- | --- |\n\n|  | 5 |  |\n| --- | --- | --- |\n\n|  | 6 |  |\n| --- | --- | --- |\n\n\n|  | 7 |  |\n| --- | --- | --- |\n\n|  | 8 |  |\n| --- | --- | --- |\n\n| Metric | MobileNet | ResNet50 | VGG16 |\n| --- | --- | --- | --- |\n| Training Accuracy | 99.97% | 97.61% | 98.31% |\n| Validation Accuracy | 99.94% | 98.92% | 98. 85% |\n| Training Loss | 0.0043 | 0.0051 | 0.0031 |\n| Validation Loss | 0.0043 | 0.0051 | 0.0031 |\n\n|  | 9 |  |\n| --- | --- | --- |\n\n|  | 10 |  |\n| --- | --- | --- |","cbCaitSOYIRNbId0","https://ap.wps.com/l/cbCaitSOYIRNbId0","pdf",1431130,"English","# MobileNet Loss Comparison\n## Loss Metrics Comparison\n## Training Epochs Comparison","[{\"question\":\"What is the training accuracy of MobileNet compared to ResNet50 and VGG16?\",\"answer\":\"MobileNet achieves a training accuracy of 99.97%, which is higher than ResNet50 (97.61%) and VGG16 (98.31%).\"},{\"question\":\"How does the validation loss of MobileNet compare across different epochs?\",\"answer\":\"The validation loss for MobileNet across 100, 200, 500, and 1000 epochs shows a decreasing trend initially, then stabilizes around 0.2-0.3, indicating consistent performance and generalization ability.\"},{\"question\":\"What is the primary focus of the comparison presented in the document?\",\"answer\":\"The document focuses on comparing the training and validation loss of the MobileNet model across various training epochs, also benchmarking it against ResNet50 and VGG16.\"}]","MobileNet Loss Comparison | PDF",1788397947]