{"id":5211,"date":"2024-03-14T02:31:52","date_gmt":"2024-03-14T02:31:52","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/how-to-utilize-pre-trained-models-for-transfer-learning-in-pytorch\/"},"modified":"2025-08-01T12:12:24","modified_gmt":"2025-08-01T12:12:24","slug":"how-to-utilize-pre-trained-models-for-transfer-learning-in-pytorch","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/how-to-utilize-pre-trained-models-for-transfer-learning-in-pytorch\/","title":{"rendered":"PyTorch Transfer Learning Guide"},"content":{"rendered":"

In PyTorch, transfer learning using a pre-trained model can be achieved through the following steps:<\/p>\n

    \n
  1. Load pre-trained model: To begin with, use the pre-trained models provided in the torchvision.models module to load a model that has been already trained, such as ResNet, VGG, and more.<\/li>\n<\/ol>\n
    import<\/span> torchvision.models as<\/span> models\r\n\r\n# Load pre-trained ResNet-50 model<\/span>\r\nmodel = models.resnet50(pretrained=True<\/span>)\r\n<\/code><\/pre>\n
      \n
    1. Alter the final layer of the model: Since transfer learning typically involves different tasks, the last layer of the pre-trained model needs to be replaced with a new fully connected layer to adapt to the requirements of the new task.<\/li>\n<\/ol>\n
      import<\/span> torch.nn as<\/span> nn\r\n\r\n# Modify the last layer of the model<\/span>\r\nnum_ftrs = model.fc.in_features\r\nmodel.fc = nn.Linear(num_ftrs, num_classes) # num_classes\u4e3a\u65b0\u4efb\u52a1\u7684\u7c7b\u522b\u6570<\/span>\r\n<\/code><\/pre>\n
        \n
      1. Set the optimizer and loss function: Customize the optimizer and loss function based on the requirements of the new task.<\/li>\n<\/ol>\n
        import<\/span> torch.optim as<\/span> optim\r\n\r\ncriterion = nn.CrossEntropyLoss()\r\noptimizer = optim.SGD(model.parameters(), lr=0.001<\/span>, momentum=0.9<\/span>)\r\n<\/code><\/pre>\n
          \n
        1. Train model: Train the model using a new dataset.<\/li>\n<\/ol>\n
          # \u8bad\u7ec3\u4ee3\u7801<\/span>\r\n<\/code><\/pre>\n
          By following the steps above, you can perform transfer learning using pre-trained models in PyTorch.<\/p>\n","protected":false},"excerpt":{"rendered":"
          In PyTorch, transfer learning using a pre-trained model can be achieved through the following steps: Load pre-trained model: To begin with, use the pre-trained models provided in the torchvision.models module to load a model that has been already trained, such as ResNet, VGG, and more. import torchvision.models as models # Load pre-trained ResNet-50 model model […]<\/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":[960,75,1218,1239,1259],"class_list":["post-5211","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-deep-learning","tag-machine-learning","tag-pre-trained-models","tag-pytorch","tag-transfer-learning"],"yoast_head":"\nPyTorch Transfer Learning Guide - Blog - Silicon Cloud<\/title>\n<meta name=\"description\" content=\"Master PyTorch transfer learning with pre-trained models. Step-by-step tutorial to adapt ResNet, VGG for your tasks.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.silicloud.com\/blog\/how-to-utilize-pre-trained-models-for-transfer-learning-in-pytorch\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PyTorch Transfer Learning Guide\" \/>\n<meta property=\"og:description\" content=\"Master PyTorch transfer learning with pre-trained models. 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