A Controlled Multi-dataset Evaluation of Custom CNNs, Pretrained Feature Extractors, and Transfer Learning

A Controlled Multi-dataset Evaluation of Custom CNNs, Pretrained Feature Extractors, and Transfer Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Controlled Multi-dataset Evaluation of Custom CNNs, Pretrained Feature Extractors, and Transfer Learning Tanzeem Maliat, Asfin Jannat Shamsi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8544413/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Convolutional neural networks (CNNs) are commonly trained using one of three paradigms: training from scratch, reuse of pretrained representations, or trans- fer learning via fine-tuning. While each strategy is widely adopted, their relative effectiveness depends strongly on dataset characteristics, computational con- straints, and deployment requirements. This paper presents a controlled and reproducible comparison of these three training paradigms across five real-world image classification datasets spanning infrastructure inspection, agricultural dis- ease recognition, and object-centric classification. All models are trained and evaluated under identical data splits, preprocessing pipelines, optimization set- tings, and evaluation metrics. Performance is reported using macro-averaged accuracy, precision, recall, and F1-score, alongside training time and model com- plexity. Rather than proposing new network architectures, this work emphasizes controlled evaluation and reproducibility to support evidence-based selection of CNN training strategies across diverse application domains. Artificial Intelligence and Machine Learning Convolutional Neural Networks Transfer Learning Feature Extraction Image Classification Deep Learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8544413","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571039136,"identity":"2a7b0cd7-4482-4507-a596-83cea13b6673","order_by":0,"name":"Tanzeem 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neural networks (CNNs) are commonly trained using one of three paradigms: training from scratch, reuse of pretrained representations, or trans- fer learning via fine-tuning. While each strategy is widely adopted, their relative effectiveness depends strongly on dataset characteristics, computational con- straints, and deployment requirements. This paper presents a controlled and reproducible comparison of these three training paradigms across five real-world image classification datasets spanning infrastructure inspection, agricultural dis- ease recognition, and object-centric classification. All models are trained and evaluated under identical data splits, preprocessing pipelines, optimization set- tings, and evaluation metrics. Performance is reported using macro-averaged accuracy, precision, recall, and F1-score, alongside training time and model com- plexity. Rather than proposing new network architectures, this work emphasizes controlled evaluation and reproducibility to support evidence-based selection of CNN training strategies across diverse application domains.\u003c/p\u003e","manuscriptTitle":"A Controlled Multi-dataset Evaluation of Custom CNNs, Pretrained Feature Extractors, and Transfer Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 04:33:02","doi":"10.21203/rs.3.rs-8544413/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b385c31-a979-45bc-84c8-5053b5cad6e3","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60767706,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-01-09T04:33:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 04:33:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8544413","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8544413","identity":"rs-8544413","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}