{"id":479546,"date":"2023-08-09T10:41:56","date_gmt":"2023-08-09T10:41:56","guid":{"rendered":""},"modified":"2023-09-05T11:19:05","modified_gmt":"2023-09-05T11:19:05","slug":"vit-vision-transformer","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/vit-vision-transformer\/","title":{"rendered":"ViT (G\u00f6r\u00fcnt\u00fc Transformat\u00f6r\u00fc)"},"content":{"rendered":"<p>ViT (G\u00f6rme Transformat\u00f6r\u00fc) hakk\u0131nda k\u0131sa bilgi<\/p>\n<p>Vision Transformer (ViT), bilgisayar g\u00f6r\u00fc\u015f\u00fc alan\u0131nda \u00f6ncelikle do\u011fal dil i\u015fleme i\u00e7in tasarlanm\u0131\u015f Transformer mimarisini kullanan yenilik\u00e7i bir sinir a\u011f\u0131 mimarisidir. Geleneksel evri\u015fimli sinir a\u011flar\u0131ndan (CNN&#039;ler) farkl\u0131 olarak ViT, g\u00f6r\u00fcnt\u00fcleri paralel olarak i\u015flemek i\u00e7in ki\u015fisel dikkat mekanizmalar\u0131n\u0131 kullan\u0131r ve \u00e7e\u015fitli bilgisayarl\u0131 g\u00f6rme g\u00f6revlerinde en son teknolojiye sahip performansa ula\u015f\u0131r.<\/p>\n<h2>ViT&#039;nin (Vision Transformer) K\u00f6keninin Tarihi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Vision Transformer, ilk olarak Google Brain&#039;den ara\u015ft\u0131rmac\u0131lar taraf\u0131ndan 2020&#039;de yay\u0131nlanan &quot;Bir G\u00f6r\u00fcnt\u00fc 16\u00d716 Kelimeye De\u011fer: Transformers for Image Recognition at Scale&quot; ba\u015fl\u0131kl\u0131 makalede tan\u0131t\u0131ld\u0131. Ara\u015ft\u0131rma, ba\u015flang\u0131\u00e7ta Transformer mimarisini uyarlama fikrinden yola \u00e7\u0131kt\u0131. Vaswani ve di\u011ferleri taraf\u0131ndan yarat\u0131lm\u0131\u015ft\u0131r. 2017&#039;de metin i\u015fleme i\u00e7in, g\u00f6r\u00fcnt\u00fc verilerini i\u015flemek \u00fczere. Sonu\u00e7, g\u00f6r\u00fcnt\u00fc tan\u0131mada \u00e7\u0131\u011f\u0131r a\u00e7an bir de\u011fi\u015fim oldu ve bu da verimlili\u011fin ve do\u011frulu\u011fun artmas\u0131na yol a\u00e7t\u0131.<\/p>\n<h2>ViT (Vision Transformer) Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>ViT, NLP&#039;de metnin bir kelime dizisi olarak ele al\u0131nmas\u0131na benzer \u015fekilde, bir g\u00f6r\u00fcnt\u00fcy\u00fc bir yama dizisi olarak ele al\u0131r. G\u00f6r\u00fcnt\u00fcy\u00fc k\u00fc\u00e7\u00fck sabit boyutlu par\u00e7alara b\u00f6ler ve bunlar\u0131 bir dizi vekt\u00f6re do\u011frusal olarak g\u00f6mer. Model daha sonra bu vekt\u00f6rleri \u00f6z-dikkat mekanizmalar\u0131n\u0131 ve ileri beslemeli a\u011flar\u0131 kullanarak i\u015fler, g\u00f6r\u00fcnt\u00fc i\u00e7indeki mekansal ili\u015fkileri ve karma\u015f\u0131k modelleri \u00f6\u011frenir.<\/p>\n<h3>Anahtar bile\u015fenler:<\/h3>\n<ul>\n<li><strong>Yamalar:<\/strong> G\u00f6r\u00fcnt\u00fcler k\u00fc\u00e7\u00fck par\u00e7alara b\u00f6l\u00fcn\u00fcr (\u00f6rn. 16\u00d716).<\/li>\n<li><strong>G\u00f6mmeler:<\/strong> Yamalar do\u011frusal yerle\u015ftirmeler yoluyla vekt\u00f6rlere d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr.<\/li>\n<li><strong>Konumsal Kodlama:<\/strong> Konum bilgisi vekt\u00f6rlere eklenir.<\/li>\n<li><strong>Ki\u015fisel Dikkat Mekanizmas\u0131:<\/strong> Model, g\u00f6r\u00fcnt\u00fcn\u00fcn t\u00fcm b\u00f6l\u00fcmleriyle ayn\u0131 anda ilgilenir.<\/li>\n<li><strong>\u0130leri Beslemeli A\u011flar:<\/strong> Bunlar, kat\u0131lan vekt\u00f6rleri i\u015flemek i\u00e7in kullan\u0131l\u0131r.<\/li>\n<\/ul>\n<h2>ViT&#039;nin (G\u00f6r\u00fcnt\u00fc Transformat\u00f6r\u00fc) \u0130\u00e7 Yap\u0131s\u0131<\/h2>\n<p>ViT&#039;nin yap\u0131s\u0131, bir ba\u015flang\u0131\u00e7 yama ve yerle\u015ftirme katman\u0131n\u0131n ard\u0131ndan bir dizi Transformer blo\u011fundan olu\u015fur. Her blokta \u00e7ok ba\u015fl\u0131 bir \u00f6z-dikkat katman\u0131 ve ileri beslemeli sinir a\u011flar\u0131 bulunur.<\/p>\n<ol>\n<li><strong>Giri\u015f Katman\u0131:<\/strong> G\u00f6r\u00fcnt\u00fc yamalara b\u00f6l\u00fcn\u00fcr ve vekt\u00f6rler olarak g\u00f6m\u00fcl\u00fcr.<\/li>\n<li><strong>Trafo Bloklar\u0131:<\/strong> A\u015fa\u011f\u0131dakileri i\u00e7eren \u00e7oklu katmanlar:\n<ul>\n<li>\u00c7ok Kafal\u0131 Ki\u015fisel Dikkat<\/li>\n<li>Normalle\u015ftirme<\/li>\n<li>\u0130leri Beslemeli Sinir A\u011f\u0131<\/li>\n<li>Ek Normalle\u015ftirme<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u00c7\u0131k\u0131\u015f Katman\u0131:<\/strong> Son bir s\u0131n\u0131fland\u0131rma ba\u015fkan\u0131.<\/li>\n<\/ol>\n<h2>ViT&#039;nin (Vision Transformer) Temel \u00d6zelliklerinin Analizi<\/h2>\n<ul>\n<li><strong>Paralel \u0130\u015fleme:<\/strong> CNN&#039;lerin aksine ViT, bilgiyi ayn\u0131 anda i\u015fler.<\/li>\n<li><strong>\u00d6l\u00e7eklenebilirlik:<\/strong> \u00c7e\u015fitli g\u00f6r\u00fcnt\u00fc boyutlar\u0131yla iyi \u00e7al\u0131\u015f\u0131r.<\/li>\n<li><strong>Genelleme:<\/strong> Farkl\u0131 bilgisayarl\u0131 g\u00f6rme g\u00f6revlerine uygulanabilir.<\/li>\n<li><strong>Veri Verimlili\u011fi:<\/strong> E\u011fitim i\u00e7in kapsaml\u0131 veri gerektirir.<\/li>\n<\/ul>\n<h2>ViT T\u00fcrleri (G\u00f6r\u00fcnt\u00fc Transformat\u00f6r\u00fc)<\/h2>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Temel ViT<\/td>\n<td>Standart ayarlara sahip orijinal model.<\/td>\n<\/tr>\n<tr>\n<td>Hibrit ViT<\/td>\n<td>Daha fazla esneklik i\u00e7in CNN katmanlar\u0131yla birle\u015ftirilmi\u015ftir.<\/td>\n<\/tr>\n<tr>\n<td>Dam\u0131t\u0131lm\u0131\u015f ViT<\/td>\n<td>Modelin daha k\u00fc\u00e7\u00fck ve daha verimli versiyonu.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>ViT (G\u00f6r\u00fcnt\u00fc Transformat\u00f6r\u00fc) Kullan\u0131m Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<h3>Kullan\u0131m Alanlar\u0131:<\/h3>\n<ul>\n<li>G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rmas\u0131<\/li>\n<li>Nesne Alg\u0131lama<\/li>\n<li>Anlamsal Segmentasyon<\/li>\n<\/ul>\n<h3>Sorunlar:<\/h3>\n<ul>\n<li>B\u00fcy\u00fck veri k\u00fcmeleri gerektirir<\/li>\n<li>Hesaplama a\u00e7\u0131s\u0131ndan pahal\u0131<\/li>\n<\/ul>\n<h3>\u00c7\u00f6z\u00fcmler:<\/h3>\n<ul>\n<li>Veri Artt\u0131rma<\/li>\n<li>\u00d6nceden e\u011fitilmi\u015f modellerin kullan\u0131lmas\u0131<\/li>\n<\/ul>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>ViT<\/th>\n<th>Geleneksel CNN<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Mimari<\/td>\n<td>Trafo bazl\u0131<\/td>\n<td>Evri\u015fim tabanl\u0131<\/td>\n<\/tr>\n<tr>\n<td>Paralel \u0130\u015fleme<\/td>\n<td>Evet<\/td>\n<td>HAYIR<\/td>\n<\/tr>\n<tr>\n<td>\u00d6l\u00e7eklenebilirlik<\/td>\n<td>Y\u00fcksek<\/td>\n<td>De\u011fi\u015fir<\/td>\n<\/tr>\n<tr>\n<td>E\u011fitim verileri<\/td>\n<td>Daha fazlas\u0131n\u0131 gerektirir<\/td>\n<td>Genellikle daha az gerektirir<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>ViT ile \u0130lgili Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>ViT, \u00e7ok modlu \u00f6\u011frenme, 3 boyutlu g\u00f6r\u00fcnt\u00fcleme ve ger\u00e7ek zamanl\u0131 i\u015fleme gibi alanlarda gelecekteki ara\u015ft\u0131rmalar\u0131n \u00f6n\u00fcn\u00fc a\u00e7\u0131yor. Devam eden inovasyon, sa\u011fl\u0131k, g\u00fcvenlik ve e\u011flence de dahil olmak \u00fczere sekt\u00f6rlerde daha verimli modellere ve daha geni\u015f uygulamalara yol a\u00e7abilir.<\/p>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya ViT (Vision Transformer) ile Nas\u0131l \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular\u0131 ViT modellerinin e\u011fitiminde etkili olabilir. \u00c7e\u015fitli ve co\u011frafi olarak da\u011f\u0131t\u0131lm\u0131\u015f veri k\u00fcmelerine eri\u015fim sa\u011flayabilir, veri gizlili\u011fini art\u0131rabilir ve da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim i\u00e7in sorunsuz ba\u011flant\u0131 sa\u011flayabilirler. Bu entegrasyon \u00f6zellikle ViT&#039;nin b\u00fcy\u00fck \u00f6l\u00e7ekli uygulamalar\u0131 i\u00e7in \u00e7ok \u00f6nemlidir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2010.11929\" target=\"_new\" rel=\"noopener nofollow\">Google Brain&#039;in ViT hakk\u0131ndaki Orijinal Makalesi<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1706.03762\" target=\"_new\" rel=\"noopener nofollow\">Trafo Mimarisi<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy Web Sitesi<\/a> ViT ile ilgili proxy sunucu \u00e7\u00f6z\u00fcmleri i\u00e7in.<\/li>\n<\/ul>\n<hr>\n<p><em>Not: Bu makale e\u011fitim ve bilgilendirme ama\u00e7l\u0131 olu\u015fturulmu\u015ftur ve ViT (Vision Transformer) alan\u0131ndaki en son ara\u015ft\u0131rma ve geli\u015fmeleri yans\u0131tacak \u015fekilde daha fazla g\u00fcncelleme gerektirebilir.<\/em><\/p>","protected":false},"featured_media":470846,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479546","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>ViT (Vision Transformer): An In-Depth Exploration<\/mark>","faq_items":[{"question":"What is the Vision Transformer (ViT)?","answer":"<p>The Vision Transformer (ViT) is a neural network architecture that utilizes the Transformer model, originally designed for natural language processing, to process images. It breaks down images into patches and processes them through self-attention mechanisms, offering parallel processing and state-of-the-art performance in computer vision tasks.<\/p>"},{"question":"How does the Vision Transformer (ViT) differ from traditional Convolutional Neural Networks (CNNs)?","answer":"<p>ViT differs from traditional CNNs by using a Transformer-based architecture instead of convolution-based layers. It processes information simultaneously across the entire image, providing higher scalability. On the downside, it often requires more training data compared to CNNs.<\/p>"},{"question":"What are the different types of ViT?","answer":"<p>There are several types of ViT, including the Base ViT (the original model), Hybrid ViT (combined with CNN layers), and Distilled ViT (a smaller and more efficient version).<\/p>"},{"question":"What are some applications and uses of ViT?","answer":"<p>ViT is used in various computer vision tasks such as image classification, object detection, and semantic segmentation.<\/p>"},{"question":"What are the main challenges in using ViT, and how can they be addressed?","answer":"<p>The main challenges in using ViT include the requirement of large datasets and its computational expense. These challenges can be addressed through data augmentation, utilizing pre-trained models, and leveraging advanced hardware.<\/p>"},{"question":"How do proxy servers, such as those provided by OneProxy, relate to ViT?","answer":"<p>Proxy servers like OneProxy can facilitate the training of ViT models by enabling access to diverse and geographically distributed datasets. They can also enhance data privacy and ensure smooth connectivity for distributed training.<\/p>"},{"question":"What are the future perspectives and technologies related to ViT?","answer":"<p>The future of ViT is promising, with potential developments in areas like multi-modal learning, 3D imaging, and real-time processing. It may lead to broader applications across various industries, including healthcare, security, and entertainment.<\/p>"},{"question":"Where can I find more information and resources related to ViT?","answer":"<p>You can find more information about ViT in the original paper by Google Brain, various academic resources, and through the OneProxy website for proxy server solutions related to ViT. Links to these resources are provided at the end of the main article.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479546","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479546\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/470846"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=479546"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}