Derin Öğrenme Modellerinin Testi için Ortak Platform Tasarımı

Year 2025, Volume: 12 Issue: 2, 693 - 709, 30.11.2025

İbrahim Kuru , Hatice Küpeli , Kerim Kürşat Çevik

https://doi.org/10.35193/bseufbd.1732011

Abstract

Bu araştırmanın amacı, kullanıcıların herhangi bir yazılım bilgisine ihtiyaç duymaksızın farklı derin öğrenme modellerini mobil cihazlar üzerinden test edebilecekleri bir uygulama geliştirmektir. Geliştirilen platform, Flutter yazılım çatısı kullanılarak hazırlanmış olup, TensorFlow Lite (TFLite) formatındaki modellerin çeşitli veri kümeleri ile denenmesini mümkün kılmaktadır. Uygulamanın temel işlevi, DeepLabV3, MobileNet ve YOLOv2 gibi yaygın kullanılan derin öğrenme mimarilerini destekleyerek model doğruluğu, işlem süresi ve bellek kullanımı gibi performans ölçütlerine dair kullanıcıya geri bildirim sunmaktır. Böylece, yalnızca belirli modellerle sınırlı kalan mevcut uygulamaların aksine, daha genel ve genişletilebilir bir yapı önerilmektedir. Çift platform desteği (Android ve iOS), sade kullanıcı arayüzü ve kolay kullanılabilirlik özellikleri sayesinde teknik bilgisi sınırlı kullanıcılar için erişilebilir bir çözüm sunulmaktadır. Elde edilen bulgular, geliştirilen platformun model test süreçlerini demokratikleştirerek derin öğrenme teknolojilerinin daha geniş kullanıcı gruplarına ulaşmasında etkili olabileceğini göstermektedir. Gelecek çalışmalarda, PyTorch ve ONNX formatlarının entegrasyonu, bulut tabanlı veri işleme sistemleriyle uyumluluk ve mobil donanım sınırlamalarına yönelik optimizasyon çalışmaları hedeflenmektedir.

Keywords

Derin Öğrenme , Mobil Platform , TensorFlow , Flutter

Designing a Common Platform for Testing Deep Learning Models

Abstract

The aim of this study is to develop a mobile application that enables users to test various deep learning models on mobile devices without requiring any programming knowledge. The platform was developed using the Flutter framework and allows for the testing of models in TensorFlow Lite (TFLite) format with different datasets. The core functionality of the application includes supporting commonly used deep learning architectures, such as DeepLabV3, MobileNet, and YOLOv2, while providing feedback on performance metrics, such as accuracy, processing time, and memory usage. Unlike existing applications that are often limited to specific models, the proposed platform offers a generalized and extensible structure. With its cross-platform compatibility (Android and iOS), user-friendly interface, and ease of use, the application provides an accessible solution for users with limited technical background. The findings indicate that the platform can contribute to democratizing the model testing process and facilitating broader access to deep learning technologies. Future work will focus on extending compatibility to other model formats, such as PyTorch and ONNX, integrating with cloud-based processing systems, and optimizing performance for mobile hardware limitations.

Keywords

Deep learning , mobile application , TensorFlow , Flutter

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