Bigisayarlı Görü Tabanlı AutoML Platformu

Year 2023, Volume: 18 Issue: 2, 425 - 433, 01.09.2023

Burak Şahin , Aytuğ Boyacı

https://doi.org/10.55525/tjst.1266144

Abstract

Teknolojik gelişmeler ve bilimsel araştırmalarla sayesinde veri üretimindeki hızlı artış, Makine Öğrenimi (ML) vb. yeni veri analiz araçlarının geliştirilmesine neden olmaktadır. Bir bulut servis sağlayıcısı olan Amazon Web Hizmetleri’nin(AWS) sadece 2021 yılında 500EB’lik veri depolandığı açıklandı. ML, geleneksel mühendislik yöntemlerine bir alternatiftir ve çözüm elde etmek için sorunun saha bilgisini gerektirmez. Bununla birlikte, ML Algoritmaları uygulanması veri setinin içeriğine göre kompleks olabilmektedir ve bu algoritmaları etkin bir şekilde kullanmak için uzman bilgisi en önemli etkendir. Bu soruna çözüm bulmak için çeşitli yöntemler geliştirilmiştir. Makine öğreniminin uygulanabileceği birçok farklı alan ve sorun bulunmaktadır. Çalışmada bilgisayarlı görü ve AutoML kullanılarak çözüm elde edilebilemek hedeflenmiştir. Bu anlamda çalışmada obje sınıflandırma, tespit etme ve segmentasyon sorunlarını çözmek için AutoML ve bilgisayarlı görü tabanlı çözümler kullanılmıştır. Hedefimiz, herhangi bir uzmanın müdahelesi olmadan çalışacak bir platform geliştirmektir. Kullanıcılar verisetlerini yükleyip, istedikleri yöntemi seçip ve başka hiçbir müdahale de bulunmadan seçtikleri sorun özellinde modellerini eğitebilemektedirler. Eğitim süreci bittikten sonra, kendi donanımlarıyla gerçek zamanlı bir şekilde platform üzerinden aktarım yapıp modellerini gerçek zamanlı bir şekilde kullanabilmektedirler.

Keywords

AutoML, Bilgisayarlı Görü, Derin Öğrenme, Makine Öğrenmesi, Nesne Sınıflandırma

Computer Vision Based AutoML Platform

Abstract

The rapid increase in data production, thanks to technological developments and scientific research, leads to the development of Machine Learning (ML) and similar new data analysis tools. It was announced that Amazon Web Services (AWS), a cloud service provider, stored 500EB of data in 2021 [1]. ML is an alternative to traditional engineering methods and does not require field knowledge of the problem to obtain a solution. However, the implementation of ML Algorithms can be complex depending on the content of the data set, and expert knowledge is the most important factor to use these algorithms effectively. Various methods have been developed to find a solution to this problem. There are many different areas and problems that machine learning can be applied to. We have limited our research to problems that can be solved using computer vision and AutoML. We have used AutoML and computer vision-based solutions to solve object classification, detection and segmentation problems. Our goal is to develop a platform that will work without the intervention of any expert. Users can load their datasets, choose the method they want, and train their models according to the problem they choose without any other intervention. After the training process is over, they can use their models in real time by transferring them over the platform in real time with their own hardware.

Keywords

AutoML, Deep Learning, Computer Vision, Machine Learning, Object Classification, Object Detection

Thanks

This study was carried out within the scope of the thesis of the National Defence University, Atatürk Institute of Strategic Studies and Graduate Education.

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