{"id":5385,"date":"2024-03-14T02:46:19","date_gmt":"2024-03-14T02:46:19","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/how-to-use-pre-trained-models-in-pytorch\/"},"modified":"2025-08-01T14:25:23","modified_gmt":"2025-08-01T14:25:23","slug":"how-to-use-pre-trained-models-in-pytorch","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/how-to-use-pre-trained-models-in-pytorch\/","title":{"rendered":"PyTorch Pre-trained Models: A Complete Guide"},"content":{"rendered":"<p>Pre-trained models can be used in PyTorch by utilizing the models module in the torchvision library. This module contains popular pre-trained models such as ResNet, VGG, and AlexNet. Below is an example of using a pre-trained ResNet model.<\/p>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">import<\/span> torch\r\n<span class=\"hljs-keyword\">import<\/span> torchvision.models <span class=\"hljs-keyword\">as<\/span> models\r\n<span class=\"hljs-keyword\">import<\/span> torchvision.transforms <span class=\"hljs-keyword\">as<\/span> transforms\r\n<span class=\"hljs-keyword\">from<\/span> PIL <span class=\"hljs-keyword\">import<\/span> Image\r\n\r\n<span class=\"hljs-comment\"># \u52a0\u8f7d\u9884\u8bad\u7ec3\u7684ResNet\u6a21\u578b<\/span>\r\nmodel = models.resnet18(pretrained=<span class=\"hljs-literal\">True<\/span>)\r\nmodel.<span class=\"hljs-built_in\">eval<\/span>()\r\n\r\n<span class=\"hljs-comment\"># \u52a0\u8f7d\u4e00\u5f20\u56fe\u7247\u8fdb\u884c\u63a8\u7406<\/span>\r\ntransform = transforms.Compose([\r\n    transforms.Resize(<span class=\"hljs-number\">256<\/span>),\r\n    transforms.CenterCrop(<span class=\"hljs-number\">224<\/span>),\r\n    transforms.ToTensor(),\r\n    transforms.Normalize(mean=[<span class=\"hljs-number\">0.485<\/span>, <span class=\"hljs-number\">0.456<\/span>, <span class=\"hljs-number\">0.406<\/span>], std=[<span class=\"hljs-number\">0.229<\/span>, <span class=\"hljs-number\">0.224<\/span>, <span class=\"hljs-number\">0.225<\/span>]),\r\n])\r\n\r\nimg = Image.<span class=\"hljs-built_in\">open<\/span>(<span class=\"hljs-string\">'image.jpg'<\/span>)\r\nimg = transform(img)\r\nimg = img.unsqueeze(<span class=\"hljs-number\">0<\/span>)  <span class=\"hljs-comment\"># \u6dfb\u52a0\u4e00\u4e2a\u7ef4\u5ea6\u4f5c\u4e3abatch<\/span>\r\n\r\n<span class=\"hljs-comment\"># \u8fdb\u884c\u63a8\u7406<\/span>\r\noutput = model(img)\r\n<\/code><\/pre>\n<p>In the example above, we first load the pre-trained ResNet model and set it to evaluation mode. Then, we load an image, preprocess it, and finally infer the output using the model. It is important to note that before inferring, we also need to call model.eval() to set the model to evaluation mode.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Pre-trained models can be used in PyTorch by utilizing the models module in the torchvision library. This module contains popular pre-trained models such as ResNet, VGG, and AlexNet. Below is an example of using a pre-trained ResNet model. import torch import torchvision.models as models import torchvision.transforms as transforms from PIL import Image # \u52a0\u8f7d\u9884\u8bad\u7ec3\u7684ResNet\u6a21\u578b model [&hellip;]<\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[960,1218,1239,1219,2370],"class_list":["post-5385","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-deep-learning","tag-pre-trained-models","tag-pytorch","tag-resnet","tag-torchvision"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PyTorch Pre-trained Models: A Complete Guide - Blog - Silicon Cloud<\/title>\n<meta name=\"description\" content=\"Learn how to use PyTorch pre-trained models like ResNet, VGG, and AlexNet with torchvision. 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