Bilgisayar g\u00f6r\u00fc\u015f\u00fc, makinelere \u00e7evrelerindeki g\u00f6rsel d\u00fcnyay\u0131 nas\u0131l anlayacaklar\u0131n\u0131 ve yorumlayacaklar\u0131n\u0131 \u00f6\u011fretir. Yapay zekan\u0131n en h\u0131zl\u0131 b\u00fcy\u00fcyen uygulamalar\u0131ndan biridir ve bir\u00e7ok sekt\u00f6rde sorunlar\u0131 \u00e7\u00f6zmek i\u00e7in kullan\u0131lmaktad\u0131r.<\/p>\n
Bilgisayar g\u00f6r\u00fc\u015f\u00fc, sa\u011fl\u0131k hizmetleri te\u015fhisine yard\u0131mc\u0131 olan bir ara\u00e7t\u0131r. Ula\u015f\u0131mda otonom ara\u00e7lar\u0131n hareketlerini takip etmek i\u00e7in kullan\u0131l\u0131r. Bankac\u0131l\u0131k ve finans alan\u0131ndaki belgeleri ve kimlik kartlar\u0131n\u0131 do\u011frular. Bunlar, bilgisayar g\u00f6r\u00fc\u015f\u00fcn\u00fcn d\u00fcnyay\u0131 de\u011fi\u015ftirmesinin bir\u00e7ok yolundan sadece birka\u00e7\u0131.<\/p>\n
<\/p>\n
Bu inan\u0131lmaz yeteneklere ula\u015fmak i\u00e7in g\u00f6r\u00fcnt\u00fc notu \u00e7ok \u00f6nemlidir. G\u00f6r\u00fcnt\u00fc ek a\u00e7\u0131klamas\u0131, bir veri etiketleme bi\u00e7imidir. AI modelinin anlayabilmesi i\u00e7in bir g\u00f6r\u00fcnt\u00fcn\u00fcn belirli b\u00f6l\u00fcmlerini etiketlemeyi i\u00e7erir. S\u00fcr\u00fcc\u00fcs\u00fcz otomobiller bu \u015fekilde trafik sinyallerini ve \u0131\u015f\u0131klar\u0131n\u0131 okuyup yorumlayabilir ve yayalardan uzakla\u015fabilir.<\/p>\n
G\u00f6r\u00fcnt\u00fclere a\u00e7\u0131klama eklemek i\u00e7in yeterli bir g\u00f6rsel veri seti ve yeterli ki\u015fi gereklidir. Bu, yapay zeka modeliniz i\u00e7in g\u00f6r\u00fcnt\u00fcleri haz\u0131rlaman\u0131za olanak tan\u0131r. G\u00f6r\u00fcnt\u00fclere a\u00e7\u0131klama eklemek, nesnelerin etraf\u0131na kutular \u00e7izmek veya hedef nesneleri ay\u0131rmak i\u00e7in \u00e7izgiler ve \u00e7okgenler kullanmak dahil olmak \u00fczere \u00e7e\u015fitli teknikler kullan\u0131larak yap\u0131labilir.<\/p>\n
AI, bir\u00e7ok yan\u0131lg\u0131ya sahip bir konudur. Labelify, makine \u00f6\u011frenimi uygulamalar\u0131na y\u00fcksek do\u011frulukla g\u00f6r\u00fcnt\u00fclere a\u00e7\u0131klama ekleyen, profesyonelce y\u00f6netilen ekipler sa\u011flar. Bu son on y\u0131lda yap\u0131ld\u0131. Bunlar, AI sistemlerine g\u00fc\u00e7 veren verileri etiketleme \u00e7abalar\u0131m\u0131zda ortadan kald\u0131rd\u0131\u011f\u0131m\u0131z baz\u0131 efsanelerdir.<\/p>\n
Otomasyon, otomatik g\u00f6r\u00fcnt\u00fc etiketleme ara\u00e7lar\u0131n\u0131n kalitesini h\u0131zla geli\u015ftiriyor. G\u00f6rsel veri k\u00fcmelerine \u00f6nceden a\u00e7\u0131klama ekleme, zamandan ve paradan tasarruf etmenize yard\u0131mc\u0131 olabilir. \u0130nsanlar\u0131n dahil oldu\u011fu otomasyon, zamandan tasarruf etmenin harika bir yoludur. Bu avantajlar \u00f6nemli bir bedelle birlikte gelir. K\u00f6t\u00fc bir \u015fekilde denetlenen \u00f6\u011frenme, modelin zaman i\u00e7inde daha az do\u011fru olmas\u0131na neden olan hatalara yol a\u00e7abilir. Bu AI kaymas\u0131 olarak bilinir.<\/p>\n
Otomatik etiketleme daha h\u0131zl\u0131d\u0131r ancak do\u011fruluktan yoksundur. Bilgisayar g\u00f6r\u00fc\u015f\u00fc, g\u00f6r\u00fcnt\u00fcleri insanlar gibi yorumlayabilir. Bu nedenle, g\u00f6r\u00fcnt\u00fc a\u00e7\u0131klama insan uzmanl\u0131\u011f\u0131 gerektirir.<\/p>\n
Bir ekranda tek bir pikseli nokta olarak g\u00f6rmek kolay olsa da, bilgisayarla g\u00f6rme verileri s\u00f6z konusu oldu\u011funda, g\u00f6r\u00fcnt\u00fc a\u00e7\u0131klamalar\u0131ndaki k\u00fc\u00e7\u00fck hatalar\u0131n bile ciddi sonu\u00e7lar\u0131 olabilir. Bir \u00f6rnek: T\u0131bbi BT taramas\u0131ndaki a\u00e7\u0131klamalar\u0131n kalitesi, hastal\u0131\u011f\u0131n te\u015fhisinde fark yaratabilir. E\u011fitim s\u0131ras\u0131ndaki tek bir hata, otonom bir arac\u0131n ya\u015fam\u0131 veya \u00f6l\u00fcm\u00fcndeki t\u00fcm fark\u0131 yaratabilir.<\/p>\n
T\u00fcm bilgisayar g\u00f6rme modelleri ya\u015fam ve \u00f6l\u00fcm\u00fc tahmin edemese de, etiketleme a\u015famas\u0131ndaki do\u011fruluk \u00f6nemli bir fakt\u00f6rd\u00fcr. D\u00fc\u015f\u00fck kaliteli a\u00e7\u0131klamal\u0131 bilgiler iki soruna neden olabilir: birincisi, model e\u011fitildi\u011finde ve ikincisi, gelecek tahminleri yapmak i\u00e7in ek a\u00e7\u0131klamay\u0131 kulland\u0131\u011f\u0131nda. Y\u00fcksek kaliteli a\u00e7\u0131klamal\u0131 verileri kullanarak y\u00fcksek performansl\u0131 bilgisayar g\u00f6r\u00fc\u015f\u00fc modelleyicileri e\u011fitmeniz gerekir.<\/p>\n
G\u00f6r\u00fcnt\u00fc a\u00e7\u0131klamas\u0131, basit, tekrarlanan bir g\u00f6rev olarak g\u00f6r\u00fclebilir. Yapay zeka konusunda herhangi bir uzmanl\u0131k gerektirmez. Ancak bu, t\u00fcm i\u015fi kendiniz yapman\u0131z gerekti\u011fi anlam\u0131na gelmez. G\u00f6r\u00fcnt\u00fc ek a\u00e7\u0131klamas\u0131, do\u011fru ara\u00e7lara ve e\u011fitime eri\u015fim gerektirir. Ayr\u0131ca, i\u015f kurallar\u0131n\u0131z, u\u00e7 vakalarla nas\u0131l ba\u015fa \u00e7\u0131k\u0131laca\u011f\u0131 ve kalite kontrol\u00fc hakk\u0131nda bilgi gerektirir. Veri bilimcilerinizin de g\u00f6r\u00fcnt\u00fcleri etiketlemesi gerekecek. Bu \u00e7ok maliyetli olabilir. \u0130\u015fin tekrar eden do\u011fas\u0131 ve kurum i\u00e7i ekipleri \u00f6l\u00e7eklendirmenin s\u0131k\u0131c\u0131 do\u011fas\u0131 nedeniyle \u00f6l\u00e7eklendirmek zor olabilir. Bu, \u00e7al\u0131\u015fan devrine yol a\u00e7abilir. Ek a\u00e7\u0131klama ekibinin kat\u0131l\u0131m\u0131n\u0131, e\u011fitimini ve y\u00f6netimini de y\u00f6netmeniz gerekecek.<\/p>\n
Verece\u011finiz en \u00f6nemli kararlardan biri, bilgisayar g\u00f6r\u00fc\u015f\u00fcn\u00fc desteklemek i\u00e7in verilerinize a\u00e7\u0131klama ekleyecek do\u011fru ki\u015fileri se\u00e7mektir. Y\u00f6netilen, harici bir ekip, uzun s\u00fcreler boyunca b\u00fcy\u00fck hacimli verilere a\u00e7\u0131klama eklemek i\u00e7in en iyisidir. Modelinizi e\u011fitirken ve test ederken bu ekiple do\u011frudan ileti\u015fim kurman\u0131z ve a\u00e7\u0131klama s\u00fcrecinizde ayarlamalar yapman\u0131z m\u00fcmk\u00fcnd\u00fcr.<\/p>\n
Kitle kaynak kullan\u0131m\u0131, b\u00fcy\u00fck bir \u00e7al\u0131\u015fan grubuna ayn\u0131 anda eri\u015fmenizi sa\u011flar. Crowdsourcing'in s\u0131n\u0131rlamalar\u0131 vard\u0131r ve bu da geni\u015f \u00f6l\u00e7ekte ek a\u00e7\u0131klama i\u00e7in kullan\u0131lmas\u0131n\u0131 zorla\u015ft\u0131r\u0131r. Kitle kaynak kullan\u0131m\u0131 anonim \u00e7al\u0131\u015fanlara dayan\u0131r. \u0130\u015f\u00e7ilerin kimlikleri zamanla de\u011fi\u015fir ve bu da onlar\u0131 kaliteden daha az sorumlu hale getirir. Kitle kaynak kullan\u0131m\u0131, zaman i\u00e7inde etki alan\u0131n\u0131za, kullan\u0131m \u00f6rne\u011finize, ek a\u00e7\u0131klama kurallar\u0131na ve di\u011fer ayr\u0131nt\u0131lara daha a\u015fina hale gelen \u00e7al\u0131\u015fanlardan yararlanman\u0131za izin vermez.<\/p>\n
Kitle kaynakl\u0131 \u00e7al\u0131\u015fanlar\u0131n ba\u015fka bir dezavantaj\u0131 daha var. Bu yakla\u015f\u0131m, kalite ek a\u00e7\u0131klamalar\u0131 i\u00e7in genellikle fikir birli\u011fi modelini kullan\u0131r. Bu, birka\u00e7 ki\u015finin ayn\u0131 g\u00f6reve atand\u0131\u011f\u0131 ve do\u011fru cevab\u0131n \u00e7al\u0131\u015fanlar\u0131n \u00e7o\u011funlu\u011fundan geldi\u011fi anlam\u0131na gelir. Ayn\u0131 g\u00f6revi birden \u00e7ok kez ger\u00e7ekle\u015ftirmenin uygun maliyetli bir yoludur.<\/p>\n
Tek bir proje \u00fczerinde \u00e7al\u0131\u015f\u0131yorsan\u0131z veya modeliniz i\u00e7in bir kavram kan\u0131t\u0131n\u0131 test ediyorsan\u0131z, kitle kaynak kullan\u0131m\u0131 iyi bir se\u00e7enek olabilir. Daha kesin olan uzun vadeli ek a\u00e7\u0131klama projeleri i\u00e7in, y\u00f6netilen d\u0131\u015f kaynakl\u0131 ekipler daha iyi bir se\u00e7im olabilir.<\/p>\n
K\u00f6t\u00fc a\u00e7\u0131klamal\u0131 g\u00f6r\u00fcnt\u00fcler, bir bilgisayarla g\u00f6rme modelini e\u011fitmek i\u00e7in kullan\u0131ld\u0131\u011f\u0131nda sorunlara neden olabilir. Kalitesiz ek a\u00e7\u0131klamalar, model do\u011frulama ve e\u011fitim s\u00fcreciniz \u00fczerinde olumsuz bir etkiye sahip olabilir. Modeliniz, ald\u0131\u011f\u0131 ek a\u00e7\u0131klamalara dayal\u0131 olarak gelecekteki kararlar\u0131 da veremeyecektir. Do\u011fru i\u015f g\u00fcc\u00fc orta\u011f\u0131yla \u00e7al\u0131\u015farak daha iyi ek a\u00e7\u0131klama kalitesi ve sonu\u00e7 olarak bilgisayar-g\u00f6r\u00fc modeliniz i\u00e7in daha iyi performans elde edebilirsiniz.<\/p>\n
Rehberimizde resim ek a\u00e7\u0131klamalar\u0131 hakk\u0131nda daha fazla bilgi edinin Bilgisayar G\u00f6r\u00fcs\u00fc i\u00e7in G\u00f6r\u00fcnt\u00fc A\u00e7\u0131klamalar\u0131<\/a>.<\/p>","protected":false},"excerpt":{"rendered":" Image Annotation in Computer Vision & its common Misconceptions Computer vision teaches machines how to understand and interpret the visual world around themselves. It is one of the fastest-growing applications of artificial intelligence and is being used across many industries to solve problems. Computer vision is a tool that aids in healthcare diagnosis. It is […]<\/p>","protected":false},"author":3,"featured_media":14329,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2869","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-annotation"],"blocksy_meta":[],"featured_image_urls":{"full":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision.jpg",2240,1260,false],"thumbnail":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-150x150.jpg",150,150,true],"medium":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-300x169.jpg",300,169,true],"medium_large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-768x432.jpg",768,432,true],"large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-1024x576.jpg",1024,576,true],"1536x1536":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-1536x864.jpg",1536,864,true],"2048x2048":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-2048x1152.jpg",2048,1152,true],"trp-custom-language-flag":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-18x10.jpg",18,10,true],"ultp_layout_landscape_large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-1200x800.jpg",1200,800,true],"ultp_layout_landscape":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-870x570.jpg",870,570,true],"ultp_layout_portrait":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-600x900.jpg",600,900,true],"ultp_layout_square":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-600x600.jpg",600,600,true],"yarpp-thumbnail":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/11\/Image-Annotation-in-Computer-Vision-120x120.jpg",120,120,true]},"post_excerpt_stackable":" Image Annotation in Computer Vision & its common Misconceptions Computer vision teaches machines how to understand and interpret the visual world around themselves. It is one of the fastest-growing applications of artificial intelligence and is being used across many industries to solve problems. Computer vision is a tool that aids in healthcare diagnosis. It is used to track the movements of autonomous vehicles in transportation. It verifies documents and identification cards in banking and finance. These are just some of the many ways that computer vision is changing the world. Image annotation is essential to achieve these amazing abilities.…<\/p>\n","category_list":"Data Annotation<\/a>","author_info":{"name":"Parth P","url":"https:\/\/www.datalabelify.com\/tr\/author\/soeuidhae\/"},"comments_num":"0 comments","_links":{"self":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/2869","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/comments?post=2869"}],"version-history":[{"count":3,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/2869\/revisions"}],"predecessor-version":[{"id":3085,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/2869\/revisions\/3085"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/media\/14329"}],"wp:attachment":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/media?parent=2869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/categories?post=2869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/tags?post=2869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}