{"id":15352,"date":"2025-03-16T15:45:14","date_gmt":"2025-03-16T12:45:14","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/pytorchta-randaugment-goruntu-guclendirme-teknigi\/"},"modified":"2025-03-16T15:45:14","modified_gmt":"2025-03-16T12:45:14","slug":"pytorchta-randaugment-goruntu-guclendirme-teknigi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/pytorchta-randaugment-goruntu-guclendirme-teknigi\/","title":{"rendered":"PyTorch&#8217;ta RandAugment: G\u00f6r\u00fcnt\u00fc G\u00fc\u00e7lendirme Tekni\u011fi"},"content":{"rendered":"<p>PyTorch&#8217;ta RandAugment: G\u00f6r\u00fcnt\u00fc G\u00fc\u00e7lendirme Tekni\u011fi<\/p>\n<p>Merhaba, Fatih Soysal burada. Bu yaz\u0131da, derin \u00f6\u011frenmede g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma performans\u0131n\u0131 art\u0131rmak i\u00e7in kullan\u0131lan g\u00fc\u00e7l\u00fc bir veri g\u00fc\u00e7lendirme tekni\u011fi olan RandAugment&#8217;\u0131 PyTorch ile nas\u0131l uygulayaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m a\u00e7\u0131klayaca\u011f\u0131m.  RandAugment, rastgele uygulanan bir dizi g\u00f6r\u00fcnt\u00fc d\u00f6n\u00fc\u015f\u00fcm\u00fc kullanarak e\u011fitim verilerinin \u00e7e\u015fitlili\u011fini art\u0131r\u0131r ve modelin daha sa\u011flam ve genelle\u015ftirilebilir hale gelmesine yard\u0131mc\u0131 olur.<\/p>\n<h2>RandAugment Nedir ve Neden Kullanmal\u0131y\u0131z?<\/h2>\n<p>G\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma modellerinin performans\u0131n\u0131 art\u0131rman\u0131n en etkili yollar\u0131ndan biri, e\u011fitim verilerinin \u00e7e\u015fitlili\u011fini art\u0131rmakt\u0131r.  Bu \u00e7e\u015fitlili\u011fi sa\u011flamak i\u00e7in kullan\u0131lan tekniklere veri g\u00fc\u00e7lendirme (data augmentation) denir.  RandAugment, bu tekniklerden biridir ve di\u011fer y\u00f6ntemlere g\u00f6re daha basit ve etkili sonu\u00e7lar verir.  \u00d6rne\u011fin, geleneksel veri g\u00fc\u00e7lendirme y\u00f6ntemleri, yatay \u00e7evirme, dikey \u00e7evirme, k\u0131rpma gibi \u00f6nceden belirlenmi\u015f d\u00f6n\u00fc\u015f\u00fcmleri uygular.  Ancak RandAugment,  bir dizi d\u00f6n\u00fc\u015f\u00fcmden rastgele se\u00e7im yapar ve her d\u00f6n\u00fc\u015f\u00fcm i\u00e7in rastgele bir \u015fiddet seviyesi belirler. Bu sayede, model daha geni\u015f bir veri da\u011f\u0131l\u0131m\u0131na maruz kal\u0131r ve a\u015f\u0131r\u0131 \u00f6\u011frenmeyi \u00f6nler. Sonu\u00e7 olarak, genelleme yetene\u011fi artar ve daha robust bir model elde edilir.<\/p>\n<h2>PyTorch ile RandAugment Uygulama<\/h2>\n<p>\u015eimdi, PyTorch kullanarak RandAugment&#8217;\u0131 nas\u0131l uygulayaca\u011f\u0131m\u0131za bakal\u0131m.  A\u015fa\u011f\u0131daki kod par\u00e7as\u0131, RandAugment&#8217;\u0131 tan\u0131mlayan bir fonksiyon i\u00e7erir.  Bu fonksiyon, belirli bir \u015fiddet seviyesi ve bir dizi d\u00f6n\u00fc\u015f\u00fcm kullanarak bir g\u00f6r\u00fcnt\u00fcy\u00fc d\u00f6n\u00fc\u015ft\u00fcr\u00fcr.<\/p>\n<pre><code class=\"language-python\">\nimport random\nfrom PIL import Image\nimport torchvision.transforms as T\n\ndef rand_augment(image, N, M):\n  ops = [T.RandomCrop(32, padding=4), T.RandomHorizontalFlip(), T.RandomVerticalFlip(), T.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1), T.RandomRotation(degrees=10)]\n  for i in range(N):\n    op = random.choice(ops)\n    mag = random.randint(0, M)\n    image = op(image)\n  return image\n\n# \u00d6rnek kullan\u0131m:\nimage = Image.open(\"image.jpg\")\naugmented_image = rand_augment(image, N=2, M=10) \naugmented_image.show()\n<\/code><\/pre>\n<p>Yukar\u0131daki kodda, <code>N<\/code> parametresi uygulanacak d\u00f6n\u00fc\u015f\u00fcm say\u0131s\u0131n\u0131, <code>M<\/code> parametresi ise her d\u00f6n\u00fc\u015f\u00fcm\u00fcn \u015fiddet seviyesini belirler.  Bu parametreleri ayarlayarak RandAugment&#8217;\u0131n etkisini kontrol edebilirsiniz.  \u00d6rne\u011fin, daha fazla d\u00f6n\u00fc\u015f\u00fcm ve daha y\u00fcksek \u015fiddet seviyesi kullanarak, veri setini daha fazla \u00e7e\u015fitlendirebilirsiniz. Ancak, \u00e7ok fazla d\u00f6n\u00fc\u015f\u00fcm modelin performans\u0131n\u0131 olumsuz etkileyebilir.<\/p>\n<h2>RandAugment&#8217;\u0131n Avantajlar\u0131 ve Dezavantajlar\u0131<\/h2>\n<p>RandAugment&#8217;\u0131n en b\u00fcy\u00fck avantaj\u0131,  basitli\u011fi ve etkinli\u011fidir.  Karma\u015f\u0131k bir yap\u0131ya sahip olmadan,  di\u011fer veri g\u00fc\u00e7lendirme y\u00f6ntemlerine g\u00f6re daha iyi sonu\u00e7lar verebilir.  Bununla birlikte,  uygun <code>N<\/code> ve <code>M<\/code> parametrelerinin se\u00e7imi,  modelin performans\u0131 \u00fczerinde \u00f6nemli bir etkiye sahiptir.  Yanl\u0131\u015f parametre se\u00e7imi, modelin performans\u0131n\u0131 d\u00fc\u015f\u00fcrebilir.  Ayr\u0131ca,  RandAugment&#8217;\u0131n hesaplama maliyeti di\u011fer basit y\u00f6ntemlere g\u00f6re biraz daha y\u00fcksek olabilir.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>RandAugment,  derin \u00f6\u011frenmede g\u00fc\u00e7l\u00fc bir veri g\u00fc\u00e7lendirme tekni\u011fidir.  PyTorch ile kolayca uygulanabilir ve modelin genelleme yetene\u011fini art\u0131rmaya yard\u0131mc\u0131 olur.  Ancak,  uygun parametrelerin se\u00e7ilmesi ve hesaplama maliyetinin g\u00f6z \u00f6n\u00fcnde bulundurulmas\u0131 \u00f6nemlidir. Daha fazla bilgi i\u00e7in <a href=\"https:\/\/dev.to\/hyperkai\/randaugment-in-pytorch-1-4dpn\">buray\u0131<\/a> ziyaret edebilirsiniz.  Kendi projelerinizde RandAugment&#8217;\u0131 deneyebilir ve sonu\u00e7lar\u0131n\u0131 g\u00f6zlemleyebilirsiniz.  Ba\u015fka sorular\u0131n\u0131z varsa,  <a href=\"https:\/\/fatihsoysal.com\">web sitemi<\/a> ziyaret edebilir veya bana ula\u015fabilirsiniz.<\/p>\n<p>Umar\u0131m bu makale faydal\u0131 olmu\u015ftur!<\/p>\n<p>#Etiketler: RandAugment, PyTorch, G\u00f6r\u00fcnt\u00fc G\u00fc\u00e7lendirme, Veri G\u00fc\u00e7lendirme, Derin \u00d6\u011frenme, G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rma, Data Augmentation,  Python,  Makine \u00d6\u011frenmesi<\/p>\n","protected":false},"excerpt":{"rendered":"PyTorch&#8217;ta RandAugment: G\u00f6r\u00fcnt\u00fc G\u00fc\u00e7lendirme Tekni\u011fi Merhaba, Fatih Soysal burada. Bu yaz\u0131da, derin \u00f6\u011frenmede g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma performans\u0131n\u0131 art\u0131rmak i\u00e7in&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-15352","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PyTorch&#039;ta RandAugment: G\u00f6r\u00fcnt\u00fc G\u00fc\u00e7lendirme Tekni\u011fi<\/title>\n<meta name=\"description\" content=\"Merhaba, Fatih Soysal burada. 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RandAugment, rastgele uygulanan bir dizi g\u00f6r\u00fcnt\u00fc d\u00f6n\u00fc\u015f\u00fcm\u00fc kullanarak e\u011fitim verilerinin \u00e7e\u015fitlili\u011fini art\u0131r\u0131r ve modelin daha sa\u011flam ve genelle\u015ftirilebilir hale gelmesine yard\u0131mc\u0131 olur.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/pytorchta-randaugment-goruntu-guclendirme-teknigi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PyTorch&#039;ta RandAugment: G\u00f6r\u00fcnt\u00fc G\u00fc\u00e7lendirme Tekni\u011fi\" \/>\n<meta property=\"og:description\" content=\"Merhaba, Fatih Soysal burada. 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Bu yaz\u0131da, derin \u00f6\u011frenmede g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma performans\u0131n\u0131 art\u0131rmak i\u00e7in kullan\u0131lan g\u00fc\u00e7l\u00fc bir veri g\u00fc\u00e7lendirme tekni\u011fi olan RandAugment'\u0131 PyTorch ile nas\u0131l uygulayaca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m a\u00e7\u0131klayaca\u011f\u0131m. 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