{"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\/cn\/wiki\/vit-vision-transformer\/","title":{"rendered":"ViT\uff08\u89c6\u89c9\u8f6c\u6362\u5668\uff09"},"content":{"rendered":"<p>\u5173\u4e8e ViT (Vision Transformer) \u7684\u7b80\u8981\u4fe1\u606f<\/p>\n<p>Vision Transformer (ViT) \u662f\u4e00\u79cd\u521b\u65b0\u7684\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\uff0c\u5b83\u5229\u7528\u4e3b\u8981\u7528\u4e8e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7684 Transformer \u67b6\u6784\uff0c\u5e94\u7528\u4e8e\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u3002\u4e0e\u4f20\u7edf\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc (CNN) \u4e0d\u540c\uff0cViT \u91c7\u7528\u81ea\u6ce8\u610f\u529b\u673a\u5236\u6765\u5e76\u884c\u5904\u7406\u56fe\u50cf\uff0c\u5728\u5404\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u4e2d\u5b9e\u73b0\u4e86\u6700\u5148\u8fdb\u7684\u6027\u80fd\u3002<\/p>\n<h2>ViT\uff08Vision Transformer\uff09\u7684\u8d77\u6e90\u5386\u53f2\u53ca\u5176\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>Vision Transformer \u6700\u521d\u662f\u7531 Google Brain \u7684\u7814\u7a76\u4eba\u5458\u5728 2020 \u5e74\u53d1\u8868\u7684\u4e00\u7bc7\u9898\u4e3a\u300a\u4e00\u5f20\u56fe\u7247\u80dc\u8fc7 16\u00d716 \u4e2a\u5355\u8bcd\uff1a\u7528\u4e8e\u5927\u89c4\u6a21\u56fe\u50cf\u8bc6\u522b\u7684 Transformers\u300b\u7684\u8bba\u6587\u4e2d\u63d0\u51fa\u7684\u3002\u8fd9\u9879\u7814\u7a76\u6e90\u4e8e\u5c06 Transformer \u67b6\u6784\uff08\u6700\u521d\u7531 Vaswani \u7b49\u4eba\u4e8e 2017 \u5e74\u4e3a\u6587\u672c\u5904\u7406\u800c\u521b\u5efa\uff09\u7528\u4e8e\u5904\u7406\u56fe\u50cf\u6570\u636e\u7684\u60f3\u6cd5\u3002\u5176\u7ed3\u679c\u662f\u56fe\u50cf\u8bc6\u522b\u53d1\u751f\u4e86\u7a81\u7834\u6027\u8f6c\u53d8\uff0c\u4ece\u800c\u63d0\u9ad8\u4e86\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<h2>\u5173\u4e8e ViT (Vision Transformer) \u7684\u8be6\u7ec6\u4fe1\u606f\uff1a\u6269\u5c55\u4e3b\u9898<\/h2>\n<p>ViT \u5c06\u56fe\u50cf\u89c6\u4e3a\u4e00\u7cfb\u5217\u5757\uff0c\u7c7b\u4f3c\u4e8e\u5728 NLP \u4e2d\u5c06\u6587\u672c\u89c6\u4e3a\u4e00\u7cfb\u5217\u5355\u8bcd\u7684\u65b9\u5f0f\u3002\u5b83\u5c06\u56fe\u50cf\u5206\u6210\u56fa\u5b9a\u5927\u5c0f\u7684\u5c0f\u5757\uff0c\u5e76\u5c06\u5b83\u4eec\u7ebf\u6027\u5d4c\u5165\u5230\u4e00\u7cfb\u5217\u5411\u91cf\u4e2d\u3002\u7136\u540e\uff0c\u8be5\u6a21\u578b\u4f7f\u7528\u81ea\u6ce8\u610f\u529b\u673a\u5236\u548c\u524d\u9988\u7f51\u7edc\u5904\u7406\u8fd9\u4e9b\u5411\u91cf\uff0c\u5b66\u4e60\u56fe\u50cf\u4e2d\u7684\u7a7a\u95f4\u5173\u7cfb\u548c\u590d\u6742\u6a21\u5f0f\u3002<\/p>\n<h3>\u5173\u952e\u90e8\u4ef6\uff1a<\/h3>\n<ul>\n<li><strong>\u8865\u4e01\uff1a<\/strong> \u56fe\u50cf\u88ab\u5206\u6210\u5c0f\u5757\uff08\u4f8b\u5982\uff0c16\u00d716\uff09\u3002<\/li>\n<li><strong>\u5d4c\u5165\uff1a<\/strong> \u901a\u8fc7\u7ebf\u6027\u5d4c\u5165\u5c06\u8865\u4e01\u8f6c\u6362\u4e3a\u5411\u91cf\u3002<\/li>\n<li><strong>\u4f4d\u7f6e\u7f16\u7801\uff1a<\/strong> \u4f4d\u7f6e\u4fe1\u606f\u88ab\u6dfb\u52a0\u5230\u77e2\u91cf\u4e2d\u3002<\/li>\n<li><strong>\u81ea\u6ce8\u610f\u529b\u673a\u5236\uff1a<\/strong> \u8be5\u6a21\u578b\u540c\u65f6\u5173\u6ce8\u56fe\u50cf\u7684\u6240\u6709\u90e8\u5206\u3002<\/li>\n<li><strong>\u524d\u9988\u7f51\u7edc\uff1a<\/strong> \u5b83\u4eec\u88ab\u7528\u6765\u5904\u7406\u5173\u6ce8\u5411\u91cf\u3002<\/li>\n<\/ul>\n<h2>ViT\uff08\u89c6\u89c9\u8f6c\u6362\u5668\uff09\u7684\u5185\u90e8\u7ed3\u6784<\/h2>\n<p>ViT \u7684\u7ed3\u6784\u7531\u521d\u59cb\u4fee\u8865\u548c\u5d4c\u5165\u5c42\u4ee5\u53ca\u968f\u540e\u7684\u4e00\u7cfb\u5217 Transformer \u5757\u7ec4\u6210\u3002\u6bcf\u4e2a\u5757\u5305\u542b\u4e00\u4e2a\u591a\u5934\u81ea\u6ce8\u610f\u529b\u5c42\u548c\u524d\u9988\u795e\u7ecf\u7f51\u7edc\u3002<\/p>\n<ol>\n<li><strong>\u8f93\u5165\u5c42\uff1a<\/strong> \u5c06\u56fe\u50cf\u5206\u6210\u591a\u4e2a\u5757\u5e76\u5d4c\u5165\u4e3a\u77e2\u91cf\u3002<\/li>\n<li><strong>\u53d8\u538b\u5668\u5757\uff1a<\/strong> \u591a\u4e2a\u5c42\uff0c\u5305\u62ec\uff1a\n<ul>\n<li>\u591a\u5934\u81ea\u6ce8\u610f\u529b<\/li>\n<li>\u6b63\u5e38\u5316<\/li>\n<li>\u524d\u9988\u795e\u7ecf\u7f51\u7edc<\/li>\n<li>\u9644\u52a0\u89c4\u8303\u5316<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u8f93\u51fa\u5c42\uff1a<\/strong> \u6700\u540e\u4e00\u4e2a\u5206\u7c7b\u4e3b\u7ba1\u3002<\/li>\n<\/ol>\n<h2>ViT\uff08Vision Transformer\uff09\u4e3b\u8981\u7279\u6027\u5206\u6790<\/h2>\n<ul>\n<li><strong>\u5e76\u884c\u5904\u7406\uff1a<\/strong> \u4e0e CNN \u4e0d\u540c\uff0cViT \u53ef\u4ee5\u540c\u65f6\u5904\u7406\u4fe1\u606f\u3002<\/li>\n<li><strong>\u53ef\u6269\u5c55\u6027\uff1a<\/strong> \u9002\u7528\u4e8e\u5404\u79cd\u56fe\u50cf\u5c3a\u5bf8\u3002<\/li>\n<li><strong>\u6982\u62ec\uff1a<\/strong> \u53ef\u4ee5\u5e94\u7528\u4e8e\u4e0d\u540c\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002<\/li>\n<li><strong>\u6570\u636e\u6548\u7387\uff1a<\/strong> \u9700\u8981\u5927\u91cf\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n<\/ul>\n<h2>ViT\uff08\u89c6\u89c9\u8f6c\u6362\u5668\uff09\u7684\u7c7b\u578b<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7c7b\u578b<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u57fa\u7840ViT<\/td>\n<td>\u5177\u6709\u6807\u51c6\u8bbe\u7f6e\u7684\u539f\u59cb\u6a21\u578b\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u6df7\u5408\u865a\u62df\u4eff\u771f<\/td>\n<td>\u4e0e CNN \u5c42\u76f8\u7ed3\u5408\u4ee5\u83b7\u5f97\u66f4\u5927\u7684\u7075\u6d3b\u6027\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u84b8\u998f\u7ef4\u751f\u7d20<\/td>\n<td>\u8be5\u6a21\u578b\u7684\u66f4\u5c0f\u4e14\u66f4\u9ad8\u6548\u7684\u7248\u672c\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>ViT (Vision Transformer) \u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848<\/h2>\n<h3>\u7528\u9014\uff1a<\/h3>\n<ul>\n<li>\u56fe\u50cf\u5206\u7c7b<\/li>\n<li>\u7269\u4f53\u68c0\u6d4b<\/li>\n<li>\u8bed\u4e49\u5206\u5272<\/li>\n<\/ul>\n<h3>\u95ee\u9898\uff1a<\/h3>\n<ul>\n<li>\u9700\u8981\u5927\u91cf\u6570\u636e\u96c6<\/li>\n<li>\u8ba1\u7b97\u6210\u672c\u9ad8\u6602<\/li>\n<\/ul>\n<h3>\u89e3\u51b3\u65b9\u6848\uff1a<\/h3>\n<ul>\n<li>\u6570\u636e\u589e\u5f3a<\/li>\n<li>\u5229\u7528\u9884\u5148\u8bad\u7ec3\u7684\u6a21\u578b<\/li>\n<\/ul>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u540c\u7c7b\u4ea7\u54c1\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u7ef4\u751f\u7d20<\/th>\n<th>\u4f20\u7edf CNN<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u5efa\u7b51\u5b66<\/td>\n<td>\u57fa\u4e8e Transformer \u7684<\/td>\n<td>\u57fa\u4e8e\u5377\u79ef<\/td>\n<\/tr>\n<tr>\n<td>\u5e76\u884c\u5904\u7406<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u4e0d<\/td>\n<\/tr>\n<tr>\n<td>\u53ef\u6269\u5c55\u6027<\/td>\n<td>\u9ad8\u7684<\/td>\n<td>\u5404\u4e0d\u76f8\u540c<\/td>\n<\/tr>\n<tr>\n<td>\u8bad\u7ec3\u6570\u636e<\/td>\n<td>\u9700\u8981\u66f4\u591a<\/td>\n<td>\u901a\u5e38\u9700\u8981\u8f83\u5c11<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e ViT \u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f<\/h2>\n<p>ViT \u4e3a\u591a\u6a21\u5f0f\u5b66\u4e60\u30013D \u6210\u50cf\u548c\u5b9e\u65f6\u5904\u7406\u7b49\u9886\u57df\u7684\u672a\u6765\u7814\u7a76\u94fa\u5e73\u4e86\u9053\u8def\u3002\u6301\u7eed\u521b\u65b0\u53ef\u80fd\u4f1a\u5e26\u6765\u66f4\u9ad8\u6548\u7684\u6a21\u578b\u548c\u66f4\u5e7f\u6cdb\u7684\u8de8\u884c\u4e1a\u5e94\u7528\uff0c\u5305\u62ec\u533b\u7597\u4fdd\u5065\u3001\u5b89\u5168\u548c\u5a31\u4e50\u3002<\/p>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e ViT (Vision Transformer) \u5173\u8054<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\uff08\u4f8b\u5982 OneProxy \u63d0\u4f9b\u7684\u4ee3\u7406\u670d\u52a1\u5668\uff09\u5728\u8bad\u7ec3 ViT \u6a21\u578b\u65b9\u9762\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u3002\u5b83\u4eec\u53ef\u4ee5\u8bbf\u95ee\u591a\u6837\u5316\u4e14\u5730\u7406\u5206\u5e03\u7684\u6570\u636e\u96c6\uff0c\u589e\u5f3a\u6570\u636e\u9690\u79c1\uff0c\u5e76\u786e\u4fdd\u5206\u5e03\u5f0f\u8bad\u7ec3\u7684\u987a\u7545\u8fde\u63a5\u3002\u8fd9\u79cd\u96c6\u6210\u5bf9\u4e8e\u5927\u89c4\u6a21\u5b9e\u65bd ViT \u5c24\u4e3a\u91cd\u8981\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2010.11929\" target=\"_new\" rel=\"noopener nofollow\">Google Brain \u5173\u4e8e ViT \u7684\u539f\u59cb\u8bba\u6587<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1706.03762\" target=\"_new\" rel=\"noopener nofollow\">Transformer \u67b6\u6784<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/cn\/\" target=\"_new\" rel=\"noopener\">OneProxy\u7f51\u7ad9<\/a> \u4e0e ViT \u76f8\u5173\u7684\u4ee3\u7406\u670d\u52a1\u5668\u89e3\u51b3\u65b9\u6848\u3002<\/li>\n<\/ul>\n<hr>\n<p><em>\u6ce8\u610f\uff1a\u672c\u6587\u65e8\u5728\u6559\u80b2\u548c\u4fe1\u606f\u76ee\u7684\uff0c\u53ef\u80fd\u9700\u8981\u8fdb\u4e00\u6b65\u66f4\u65b0\u4ee5\u53cd\u6620 ViT\uff08\u89c6\u89c9\u8f6c\u6362\u5668\uff09\u9886\u57df\u7684\u6700\u65b0\u7814\u7a76\u548c\u53d1\u5c55\u3002<\/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\/cn\/wp-json\/wp\/v2\/wiki\/479546","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/479546\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/470846"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=479546"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}