PyTorch’ta RandomResizedCrop: G\u00f6r\u00fcnt\u00fc Veri Art\u0131rma Tekni\u011fi<\/p>\n
Derin \u00f6\u011frenme projelerinde, \u00f6zellikle g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma gibi g\u00f6revlerde, yeterli miktarda veriye sahip olmak hayati \u00f6nem ta\u015f\u0131r. Yetersiz veri, modelin genelleme yetene\u011fini olumsuz etkileyerek performans d\u00fc\u015f\u00fc\u015f\u00fcne yol a\u00e7abilir. Bu sorunun \u00fcstesinden gelmek i\u00e7in kullan\u0131lan etkili y\u00f6ntemlerden biri de veri art\u0131rmad\u0131r. PyTorch, bu konuda zengin bir k\u00fct\u00fcphane sunar ve bu k\u00fct\u00fcphanede Peki, \u0130\u015fte PyTorch kullanarak Bu kodda, Daha fazla bilgi ve \u00f6rnekler i\u00e7in PyTorch resmi dok\u00fcmantasyonuna<\/a> g\u00f6z atabilirsiniz. Derin \u00f6\u011frenme yolculu\u011funuzda ba\u015far\u0131lar dilerim! Bu konuda daha fazla bilgi edinmek i\u00e7in fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz.<\/p>\n #Etiketler: PyTorch, RandomResizedCrop, veri art\u0131rma, g\u00f6r\u00fcnt\u00fc i\u015fleme, derin \u00f6\u011frenme, yapay zeka, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, model performans\u0131, veri \u00e7e\u015fitlili\u011fi, overfitting<\/p>\n <\/body>RandomResizedCrop<\/code> fonksiyonu, g\u00f6r\u00fcnt\u00fclerin boyutunu ve konumunu rastgele de\u011fi\u015ftirerek veri \u00e7e\u015fitlili\u011fini art\u0131rmada \u00f6nemli bir rol oynar.<\/p>\nRandomResizedCrop<\/code> tam olarak ne yapar? \u00d6zetle, bu fonksiyon girdi olarak verilen bir g\u00f6r\u00fcnt\u00fcy\u00fc rastgele bir alandan k\u0131rpar ve daha sonra belirtilen bir boyuta yeniden boyutland\u0131r\u0131r. Bu i\u015flem, modeli farkl\u0131 a\u00e7\u0131lar, \u00f6l\u00e7ekler ve kesimlerle e\u011fiterek daha sa\u011flam ve genelle\u015ftirilebilir hale getirir. Sonu\u00e7 olarak, a\u015f\u0131r\u0131 \u00f6\u011frenmeyi (overfitting) \u00f6nler ve modelin performans\u0131n\u0131 iyile\u015ftirir.<\/p>\nRandomResizedCrop Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n
RandomResizedCrop<\/code>, \u00fc\u00e7 ana ad\u0131mda \u00e7al\u0131\u015f\u0131r: <\/p>\n\n
PyTorch ile RandomResizedCrop Uygulamas\u0131<\/h2>\n
RandomResizedCrop<\/code>‘u nas\u0131l kullanabilece\u011finize dair bir \u00f6rnek kod:<\/p>\n\nimport torch\nfrom torchvision import transforms\n\n# D\u00f6n\u00fc\u015f\u00fcm nesnesi olu\u015fturma\ntransform = transforms.RandomResizedCrop(size=(224, 224), scale=(0.5, 1.0))\n\n# \u00d6rnek g\u00f6r\u00fcnt\u00fc (tensor)\nimage = torch.randn(3, 256, 256) # 3 kanal, 256x256 boyutunda\n\n# D\u00f6n\u00fc\u015f\u00fcm\u00fc uygulama\ntransformed_image = transform(image)\n\n# Sonucu yazd\u0131rma\nprint(transformed_image.shape) \n<\/code><\/pre>\nRandomResizedCrop<\/code> nesnesi, girdi g\u00f6r\u00fcnt\u00fclerini 224×224 boyutuna yeniden boyutland\u0131rarak, \u00f6l\u00e7e\u011fi %50 ile %100 aras\u0131nda rastgele ayarlar. scale<\/code> parametresi ile k\u0131rpma i\u015flemi i\u00e7in g\u00f6r\u00fcnt\u00fcn\u00fcn ne kadar\u0131n\u0131n kullan\u0131laca\u011f\u0131n\u0131 kontrol edebiliriz. Daha k\u00fc\u00e7\u00fck bir scale de\u011feri, daha fazla k\u0131rpmaya ve daha fazla veri art\u0131rmaya yol a\u00e7ar. size<\/code> parametresi ise, hedef g\u00f6r\u00fcnt\u00fcn\u00fcn boyutunu belirler.<\/p>\nKullan\u0131m Senaryolar\u0131 ve \u0130pu\u00e7lar\u0131<\/h2>\n
RandomResizedCrop<\/code>, \u00f6zellikle s\u0131n\u0131rl\u0131 veri k\u00fcmelerine sahip g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma g\u00f6revlerinde olduk\u00e7a faydal\u0131d\u0131r. Ancak, a\u015f\u0131r\u0131 kullan\u0131m\u0131 da model performans\u0131n\u0131 olumsuz etkileyebilir. Bu nedenle, scale<\/code> ve ratio<\/code> parametrelerini dikkatlice ayarlamak \u00f6nemlidir. Ayr\u0131ca, di\u011fer veri art\u0131rma teknikleriyle birlikte kullan\u0131ld\u0131\u011f\u0131nda daha etkili sonu\u00e7lar elde edilebilir. \u00d6rne\u011fin, yatay \u00e7evirme (RandomHorizontalFlip<\/code>) veya renk ayarlamalar\u0131 (ColorJitter<\/code>) ile birlikte kullan\u0131labilir.<\/p>\n
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