{"id":29627,"date":"2025-09-17T11:30:26","date_gmt":"2025-09-17T08:30:26","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/cnn-egitiminden-sonra-efficientnet-b2-gelismis-goruntu-siniflandirma\/"},"modified":"2025-09-17T11:30:26","modified_gmt":"2025-09-17T08:30:26","slug":"cnn-egitiminden-sonra-efficientnet-b2-gelismis-goruntu-siniflandirma","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/cnn-egitiminden-sonra-efficientnet-b2-gelismis-goruntu-siniflandirma\/","title":{"rendered":"CNN E\u011fitiminden Sonra EfficientNet-B2: Geli\u015fmi\u015f G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rma"},"content":{"rendered":"

CNN E\u011fitiminden Sonra EfficientNet-B2: Geli\u015fmi\u015f G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rma<\/p>\n

Derin \u00f6\u011frenme d\u00fcnyas\u0131nda, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma problemlerini \u00e7\u00f6zmek i\u00e7in Convolutional Neural Networks (CNN’ler) yayg\u0131n olarak kullan\u0131l\u0131r. Ancak, daha y\u00fcksek do\u011fruluk ve verimlilik i\u00e7in daha geli\u015fmi\u015f mimarilere ihtiya\u00e7 duyulur. Bu makale, CNN e\u011fitiminizi tamamlad\u0131ktan sonra EfficientNet-B2 mimarisine ge\u00e7i\u015fi ad\u0131m ad\u0131m a\u00e7\u0131klayacak ve bu g\u00fc\u00e7l\u00fc mimarinin avantajlar\u0131n\u0131 ve kullan\u0131m\u0131n\u0131 detayl\u0131 olarak inceleyecektir. EfficientNet-B2’nin nas\u0131l kullan\u0131laca\u011f\u0131, performans kar\u015f\u0131la\u015ft\u0131rmalar\u0131 ve olas\u0131 sorunlar ile \u00e7\u00f6z\u00fcm yollar\u0131 ele al\u0131nacakt\u0131r.<\/p>\n

CNN’lerden EfficientNet-B2’ye Ge\u00e7i\u015f: Neden ve Nas\u0131l?<\/h2>\n

Bir CNN e\u011fitimini tamamlad\u0131ktan sonra, performans iyile\u015ftirme aray\u0131\u015f\u0131 do\u011fal bir sonucudur. EfficientNet mimarisi, bu aray\u0131\u015fta \u00f6n plana \u00e7\u0131kan g\u00fc\u00e7l\u00fc bir \u00e7\u00f6z\u00fcmd\u00fcr. EfficientNet-B2, EfficientNet ailesinin bir \u00fcyesi olup, \u00f6l\u00e7eklendirme y\u00f6ntemiyle tasarlanm\u0131\u015ft\u0131r ve hem do\u011fruluk hem de hesaplama maliyeti a\u00e7\u0131s\u0131ndan dengelenmi\u015ftir. Bu, daha az hesaplama g\u00fcc\u00fcyle daha iyi sonu\u00e7lar elde etmeyi m\u00fcmk\u00fcn k\u0131lar. CNN’lerden EfficientNet-B2’ye ge\u00e7i\u015f, daha y\u00fcksek do\u011fruluk hedefleyen ve hesaplama kaynaklar\u0131n\u0131 daha verimli kullanmak isteyen projeler i\u00e7in idealdir. Bu ge\u00e7i\u015f, model mimarisinin de\u011fi\u015ftirilmesi, hiperparametre optimizasyonu ve e\u011fitim s\u00fcrecinin yeniden d\u00fczenlenmesini i\u00e7erir. \u00d6rne\u011fin, daha az veriyle daha iyi sonu\u00e7lar elde etmek i\u00e7in transfer \u00f6\u011frenme tekniklerini kullanabiliriz. A\u015fa\u011f\u0131da ad\u0131m ad\u0131m ge\u00e7i\u015f s\u00fcreci detayl\u0131 olarak a\u00e7\u0131klanm\u0131\u015ft\u0131r.<\/p>\n

Temel Kavramlar: CNN ve EfficientNet Mimarisinin Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/h2>\n

\u00d6ncelikle, temel kavramlar\u0131 a\u00e7\u0131kl\u0131\u011fa kavu\u015ftural\u0131m. Convolutional Neural Networks (CNN’ler), g\u00f6r\u00fcnt\u00fc verilerinde yerel \u00f6zelliklerin \u00e7\u0131kar\u0131lmas\u0131nda uzmanla\u015fm\u0131\u015f derin \u00f6\u011frenme mimarileridir. \u00c7e\u015fitli katmanlardan (konvol\u00fcsyonel, havuzlama, tam ba\u011fl\u0131 katmanlar vb.) olu\u015furlar. Ancak, CNN’lerin karma\u015f\u0131kl\u0131\u011f\u0131 ve parametre say\u0131s\u0131, \u00f6zellikle b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in hesaplama maliyetini art\u0131rabilir. EfficientNet mimarisi, “compound scaling” adl\u0131 bir y\u00f6ntem kullanarak, CNN’lerin performans\u0131n\u0131 optimize eder. Bu y\u00f6ntem, derinlik, geni\u015flik ve \u00e7\u00f6z\u00fcn\u00fcrl\u00fc\u011f\u00fc dengeli bir \u015fekilde \u00f6l\u00e7eklendirerek, daha y\u00fcksek do\u011fruluk ve verimlilik sa\u011flar. Bu, CNN’lere g\u00f6re daha az parametre kullan\u0131rken, daha iyi performans g\u00f6stermelerini sa\u011flar. K\u0131saca, EfficientNet-B2, daha kompakt ve verimli bir mimari sunarken, CNN’lere g\u00f6re daha y\u00fcksek do\u011fruluk oranlar\u0131 elde edilmesini sa\u011flar. Bu nedenle, kaynak k\u0131s\u0131tl\u0131 ortamlarda veya daha h\u0131zl\u0131 \u00e7\u0131kar\u0131m s\u00fcrelerine ihtiya\u00e7 duyulan uygulamalar i\u00e7in idealdir.<\/p>\n

EfficientNet-B2’yi Uygulamaya Koyma: Ad\u0131m Ad\u0131m K\u0131lavuz<\/h2>\n

EfficientNet-B2’yi uygulamaya koymak i\u00e7in, \u00f6ncelikle uygun bir derin \u00f6\u011frenme k\u00fct\u00fcphanesi (\u00f6rne\u011fin, TensorFlow veya PyTorch) se\u00e7meniz gerekir. Ard\u0131ndan, \u00f6nceden e\u011fitilmi\u015f bir EfficientNet-B2 modeli y\u00fckleyebilir veya s\u0131f\u0131rdan e\u011fitebilirsiniz. \u00d6nceden e\u011fitilmi\u015f modeller, transfer \u00f6\u011frenmesi yakla\u015f\u0131m\u0131yla, daha az veriyle daha h\u0131zl\u0131 ve etkili bir e\u011fitim sa\u011flar. A\u015fa\u011f\u0131da, TensorFlow kullanarak ad\u0131m ad\u0131m bir uygulama \u00f6rne\u011fi verilmi\u015ftir:<\/p>\n

\nimport tensorflow as tf\n\n# \u00d6nceden e\u011fitilmi\u015f EfficientNet-B2 modelini y\u00fckleyin\nmodel = tf.keras.applications.EfficientNetB2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n\n# Kendi verileriniz i\u00e7in \u00fcst katman\u0131 ekleyin\nx = model.output\nx = tf.keras.layers.GlobalAveragePooling2D()(x)\nx = tf.keras.layers.Dense(1024, activation='relu')(x) # \u00d6rnek: 1024 n\u00f6ronlu tam ba\u011fl\u0131 katman\npredictions = tf.keras.layers.Dense(num_classes, activation='softmax')(x) # num_classes s\u0131n\u0131f say\u0131s\u0131n\u0131 belirtir\n\n# Yeni modeli olu\u015fturun\nmodel_final = tf.keras.models.Model(inputs=model.input, outputs=predictions)\n\n# Modeli derleyin\nmodel_final.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n# Modeli e\u011fitin\nmodel_final.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))\n<\/pre>\n
<\/code><\/p>\n
Bu \u00f6rnekte,  imagenet<\/code> a\u011f\u0131rl\u0131klar\u0131yla \u00f6nceden e\u011fitilmi\u015f bir EfficientNet-B2 modeli y\u00fckleniyor ve kendi verileriniz i\u00e7in \u00fcst katmanlar ekleniyor.  Daha sonra model derleniyor ve e\u011fitim verileriyle e\u011fitiliyor.  E\u011fitim parametreleri (epoch say\u0131s\u0131, optimizer, loss fonksiyonu vb.)  verilerinize ve gereksinimlerinize g\u00f6re ayarlanmal\u0131d\u0131r.  Unutmay\u0131n ki,  bu sadece basit bir \u00f6rnektir ve daha karma\u015f\u0131k senaryolar i\u00e7in daha geli\u015fmi\u015f teknikler kullanman\u0131z gerekebilir.<\/p>\n
\u0130leri D\u00fczey Teknikler: Hiperparametre Optimizasyonu ve Transfer \u00d6\u011frenmesi<\/h2>\n
EfficientNet-B2'nin performans\u0131n\u0131 daha da iyile\u015ftirmek i\u00e7in, hiperparametre optimizasyonu ve transfer \u00f6\u011frenmesi tekniklerinden yararlanabilirsiniz.  Hiperparametre optimizasyonu, \u00f6\u011frenme oran\u0131, batch boyutu gibi parametreleri optimize ederek modelin performans\u0131n\u0131 art\u0131rmay\u0131 ama\u00e7lar.  Grid search, rastgele arama veya bayesian optimizasyon gibi y\u00f6ntemler kullan\u0131labilir. Transfer \u00f6\u011frenmesi ise, \u00f6nceden e\u011fitilmi\u015f bir modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 kullanarak,  k\u0131s\u0131tl\u0131 veri setlerinde bile iyi performans elde etmeyi sa\u011flar.  Bu teknik, \u00f6zellikle k\u00fc\u00e7\u00fck veri setleriyle \u00e7al\u0131\u015f\u0131rken olduk\u00e7a etkilidir.  \u00d6rne\u011fin, \u00f6nceden e\u011fitilmi\u015f bir EfficientNet-B2 modelinin son katmanlar\u0131n\u0131 kendi verileriniz i\u00e7in yeniden e\u011fiterek,  hem zaman hem de hesaplama g\u00fcc\u00fc tasarrufu sa\u011flayabilirsiniz.  Ayr\u0131ca,  veri art\u0131rma teknikleri (data augmentation) kullanarak,  e\u011fitim verilerinizin \u00e7e\u015fitlili\u011fini art\u0131rabilir ve modelin genelleme yetene\u011fini iyile\u015ftirebilirsiniz.<\/p>\n
Ger\u00e7ek D\u00fcnya Senaryolar\u0131: Vaka \u00c7al\u0131\u015fmalar\u0131<\/h2>\n
EfficientNet-B2, \u00e7e\u015fitli ger\u00e7ek d\u00fcnya uygulamalar\u0131nda ba\u015far\u0131yla kullan\u0131lm\u0131\u015ft\u0131r.  \u00d6rne\u011fin, t\u0131bbi g\u00f6r\u00fcnt\u00fclemede,  kanser h\u00fccrelerinin tespiti i\u00e7in kullan\u0131labilir.  Tar\u0131m sekt\u00f6r\u00fcnde,  bitki hastal\u0131klar\u0131n\u0131n tespiti veya verim tahmini i\u00e7in uygulanabilir.  G\u00fcvenlik sekt\u00f6r\u00fcnde,  y\u00fcz tan\u0131ma veya nesne tespiti sistemlerinde kullan\u0131labilir.  Bu uygulamalar\u0131n her birinde, EfficientNet-B2'nin y\u00fcksek do\u011frulu\u011fu ve verimlili\u011fi,  sistemlerin performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r.  \u00d6rne\u011fin, bir t\u0131bbi g\u00f6r\u00fcnt\u00fcleme sisteminde,  do\u011fru tan\u0131 koymak i\u00e7in daha y\u00fcksek do\u011fruluk oranlar\u0131na ihtiya\u00e7 duyulur.  EfficientNet-B2, bu y\u00fcksek do\u011fruluk oranlar\u0131n\u0131 sa\u011flayarak,  yanl\u0131\u015f te\u015fhislerin say\u0131s\u0131n\u0131 azaltmaya yard\u0131mc\u0131 olur.  Benzer \u015fekilde,  tar\u0131m sekt\u00f6r\u00fcnde,  verim tahmini i\u00e7in daha do\u011fru ve h\u0131zl\u0131 sonu\u00e7lara ihtiya\u00e7 vard\u0131r.  EfficientNet-B2, bu ihtiyac\u0131 kar\u015f\u0131layarak,  tar\u0131m verimlili\u011fini art\u0131rmaya katk\u0131da bulunur.<\/p>\n
Performans Kar\u015f\u0131la\u015ft\u0131rmalar\u0131: EfficientNet-B2 vs. Di\u011fer Mimariler<\/h2>\n
EfficientNet-B2'nin performans\u0131n\u0131 di\u011fer mimarilerle kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in,  birka\u00e7 farkl\u0131 veri seti \u00fczerinde testler yap\u0131lmas\u0131 ve sonu\u00e7lar\u0131n kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131 gerekir.  Genel olarak,  EfficientNet-B2,  daha az parametre kullan\u0131rken,  di\u011fer bir\u00e7ok mimariye k\u0131yasla daha y\u00fcksek do\u011fruluk oranlar\u0131 elde eder.  A\u015fa\u011f\u0131daki tabloda,  baz\u0131 pop\u00fcler mimarilerle yap\u0131lan bir performans kar\u015f\u0131la\u015ft\u0131rmas\u0131 \u00f6rne\u011fi verilmi\u015ftir:<\/p>\n\n\n\n\n\n
Mimari<\/th>\nDo\u011fruluk (%)<\/th>\nParametre Say\u0131s\u0131<\/th>\n<\/tr>\n
ResNet-50<\/td>\n76<\/td>\n25 milyon<\/td>\n<\/tr>\n
Inception-V3<\/td>\n78<\/td>\n25 milyon<\/td>\n<\/tr>\n
EfficientNet-B2<\/td>\n80<\/td>\n7 milyon<\/td>\n<\/tr>\n<\/table>\n
Bu tabloda g\u00f6r\u00fcld\u00fc\u011f\u00fc gibi,  EfficientNet-B2,  daha az parametre kullanmas\u0131na ra\u011fmen,  ResNet-50 ve Inception-V3'ten daha y\u00fcksek do\u011fruluk oranlar\u0131 elde etmi\u015ftir.  Bu,  EfficientNet mimarisinin verimlili\u011fini ve performans\u0131n\u0131 g\u00f6stermektedir.  Ancak,  bu sonu\u00e7lar veri setine ve e\u011fitim ko\u015fullar\u0131na ba\u011fl\u0131 olarak de\u011fi\u015febilir.<\/p>\n
G\u00fcvenlik Uyar\u0131lar\u0131 ve Dikkat Edilmesi Gerekenler<\/h2>\n
Dikkat!  EfficientNet-B2 gibi derin \u00f6\u011frenme modellerini kullan\u0131rken,  g\u00fcvenlik a\u00e7\u0131klar\u0131na kar\u015f\u0131 dikkatli olmak \u00f6nemlidir.  E\u011fitim verilerindeki \u00f6nyarg\u0131lar,  modelin yanl\u0131\u015f sonu\u00e7lar \u00fcretmesine neden olabilir.  Bu nedenle,  e\u011fitim verilerinin \u00e7e\u015fitlili\u011fi ve kalitesi \u00e7ok \u00f6nemlidir.  Ayr\u0131ca,  modelin \u00e7\u0131kt\u0131lar\u0131n\u0131n do\u011frulanmas\u0131 ve g\u00fcvenilirli\u011finin test edilmesi gerekir.  \u00d6zellikle kritik uygulamalarda,  modelin performans\u0131 s\u00fcrekli olarak izlenmeli ve gerekirse g\u00fcncellenmelidir.  Yanl\u0131\u015f veya eksik verilerle e\u011fitilmi\u015f bir model,  beklenmedik ve zararl\u0131 sonu\u00e7lar do\u011furabilir.  Bu nedenle,  veri kalitesi ve g\u00fcvenli\u011fi en \u00fcst d\u00fczeyde tutulmal\u0131d\u0131r.<\/p>\n
Sonu\u00e7 ve S\u0131k\u00e7a Sorulan Sorular<\/h2>\n
Bu makalede,  CNN e\u011fitiminizden sonra EfficientNet-B2 mimarisine ge\u00e7i\u015f s\u00fcrecini ad\u0131m ad\u0131m a\u00e7\u0131klad\u0131k.  EfficientNet-B2'nin y\u00fcksek do\u011frulu\u011fu ve verimlili\u011fi,  \u00e7e\u015fitli ger\u00e7ek d\u00fcnya uygulamalar\u0131nda ba\u015far\u0131l\u0131 sonu\u00e7lar elde edilmesini sa\u011flar.  Ancak,  g\u00fcvenlik a\u00e7\u0131klar\u0131na kar\u015f\u0131 dikkatli olmak ve modelin performans\u0131n\u0131 s\u00fcrekli izlemek \u00f6nemlidir.  Daha fazla bilgi i\u00e7in (https:\/\/fatihsoysal.com) inceleyebilirsiniz.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular:<\/h3>\n
    \n
  • EfficientNet-B2'nin di\u011fer EfficientNet varyantlar\u0131ndan fark\u0131 nedir?<\/b> EfficientNet-B2, EfficientNet ailesinin bir \u00fcyesidir ve belirli bir \u00f6l\u00e7eklendirme ile tasarlanm\u0131\u015ft\u0131r. Di\u011fer varyantlara g\u00f6re daha b\u00fcy\u00fck ve daha y\u00fcksek do\u011fruluk sunar ancak daha fazla hesaplama g\u00fcc\u00fc gerektirir.  <\/li>\n
  • \u00d6nceden e\u011fitilmi\u015f bir model kullanman\u0131n avantajlar\u0131 nelerdir?<\/b> \u00d6nceden e\u011fitilmi\u015f modeller, s\u0131f\u0131rdan e\u011fitim yapmaya k\u0131yasla \u00e7ok daha az veri ile daha h\u0131zl\u0131 ve etkili sonu\u00e7lar verir. Transfer \u00f6\u011frenmesi sayesinde, s\u0131n\u0131rl\u0131 veri setlerinde bile ba\u015far\u0131l\u0131 performanslar elde edilebilir.<\/li>\n
  • Hangi donan\u0131m EfficientNet-B2'yi \u00e7al\u0131\u015ft\u0131rmak i\u00e7in yeterlidir?<\/b> EfficientNet-B2'yi \u00e7al\u0131\u015ft\u0131rmak i\u00e7in orta seviye bir GPU \u00f6nerilir. Ancak, daha az g\u00fc\u00e7l\u00fc donan\u0131mda da transfer \u00f6\u011frenmesi ve model s\u0131k\u0131\u015ft\u0131rmas\u0131 gibi teknikler kullan\u0131larak \u00e7al\u0131\u015ft\u0131r\u0131labilir.<\/li>\n
  • EfficientNet-B2'nin performans\u0131n\u0131 nas\u0131l daha da iyile\u015ftirebilirim?<\/b> Hiperparametre optimizasyonu, veri art\u0131rma, farkl\u0131 optimizasyon algoritmalar\u0131 ve daha geli\u015fmi\u015f e\u011fitim stratejileri kullanarak modelin performans\u0131 art\u0131r\u0131labilir.<\/li>\n
  • Kendi veri setim i\u00e7in EfficientNet-B2'yi nas\u0131l \u00f6zelle\u015ftirebilirim?<\/b>  Modelin son katmanlar\u0131n\u0131 de\u011fi\u015ftirerek, kendi veri setinizin s\u0131n\u0131f say\u0131s\u0131na g\u00f6re uyarlayabilirsiniz.  Transfer \u00f6\u011frenmesi teknikleri kullanarak, \u00f6nceden e\u011fitilmi\u015f modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 ba\u015flang\u0131\u00e7 noktas\u0131 olarak kullanabilirsiniz.<\/li>\n<\/ul>\n
    Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"CNN E\u011fitiminden Sonra EfficientNet-B2: Geli\u015fmi\u015f G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rma Derin \u00f6\u011frenme d\u00fcnyas\u0131nda, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma problemlerini \u00e7\u00f6zmek i\u00e7in Convolutional Neural Networks…","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-29627","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nCNN E\u011fitiminden Sonra EfficientNet-B2: Geli\u015fmi\u015f G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rma<\/title>\n<meta name=\"description\" content=\"Derin \u00f6\u011frenme d\u00fcnyas\u0131nda, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma problemlerini \u00e7\u00f6zmek i\u00e7in Convolutional Neural Networks (CNN'ler) yayg\u0131n olarak kullan\u0131l\u0131r. Ancak, daha y\u00fcksek do\u011fruluk ve verimlilik i\u00e7in daha geli\u015fmi\u015f mimarilere ihtiya\u00e7 duyulur. Bu makale, CNN e\u011fitiminizi tamamlad\u0131ktan sonra EfficientNet-B2 mimarisine ge\u00e7i\u015fi ad\u0131m ad\u0131m a\u00e7\u0131klayacak ve bu g\u00fc\u00e7l\u00fc mimarinin avantajlar\u0131n\u0131 ve kullan\u0131m\u0131n\u0131 detayl\u0131 olarak inceleyecektir. 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