experiment.zh.json

{
 "<h1>Training <a href=\"index.html\">U-Net</a></h1>\n<p>This trains a <a href=\"index.html\">U-Net</a> model on <a href=\"carvana.html\">Carvana dataset</a>. You can find the download instructions <a href=\"https://www.kaggle.com/competitions/carvana-image-masking-challenge/data\">on Kaggle</a>.</p>\n<p>Save the training images inside <span translate=no>_^_0_^_</span> folder and the masks in <span translate=no>_^_1_^_</span> folder.</p>\n<p>For simplicity, we do not do a training and validation split.</p>\n": "<h1>\u8bad\u7ec3 <a href=\"index.html\">U-Net</a></h1>\n<p>\u8fd9\u4f1a\u5728 <a href=\"carvana.html\">Carvana \u6570\u636e\u96c6</a>\u4e0a\u8bad\u7ec3\u4e00\u4e2a <a href=\"index.html\">U-Net</a> \u6a21\u578b\u3002\u4f60\u53ef\u4ee5<a href=\"https://www.kaggle.com/competitions/carvana-image-masking-challenge/data\">\u5728 Kaggle \u4e0a</a>\u627e\u5230\u4e0b\u8f7d\u8bf4\u660e\u3002</p>\n<p>\u5c06\u8bad\u7ec3\u56fe\u50cf\u4fdd\u5b58\u5728<span translate=no>_^_0_^_</span>\u6587\u4ef6\u5939\u4e2d\uff0c\u5c06\u8499\u7248\u4fdd\u5b58\u5728<span translate=no>_^_1_^_</span>\u6587\u4ef6\u5939\u4e2d\u3002</p>\n<p>\u4e3a\u7b80\u5355\u8d77\u89c1\uff0c\u6211\u4eec\u4e0d\u8fdb\u884c\u8bad\u7ec3\u548c\u9a8c\u8bc1\u62c6\u5206\u3002</p>\n",
 "<h2>Configurations</h2>\n": "<h2>\u914d\u7f6e</h2>\n",
 "<h3>Sample images</h3>\n": "<h3>\u6837\u672c\u56fe\u7247</h3>\n",
 "<h3>Train for an epoch</h3>\n": "<h3>\u8bad\u7ec3\u4e00\u4e2a\u65f6\u4ee3</h3>\n",
 "<h3>Training loop</h3>\n": "<h3>\u8bad\u7ec3\u5faa\u73af</h3>\n",
 "<p> </p>\n": "<p></p>\n",
 "<p><a href=\"index.html\">U-Net</a> model </p>\n": "<p><a href=\"index.html\">U-Net</a> \u6a21\u578b</p>\n",
 "<p>Adam optimizer </p>\n": "<p>Adam \u4f18\u5316\u5668</p>\n",
 "<p>Batch size </p>\n": "<p>\u6279\u91cf\u5927\u5c0f</p>\n",
 "<p>Calculate loss </p>\n": "<p>\u8ba1\u7b97\u635f\u5931</p>\n",
 "<p>Compute gradients </p>\n": "<p>\u8ba1\u7b97\u68af\u5ea6</p>\n",
 "<p>Create configurations </p>\n": "<p>\u521b\u5efa\u914d\u7f6e</p>\n",
 "<p>Create dataloader </p>\n": "<p>\u521b\u5efa\u6570\u636e\u52a0\u8f7d\u5668</p>\n",
 "<p>Create experiment </p>\n": "<p>\u521b\u5efa\u5b9e\u9a8c</p>\n",
 "<p>Create optimizer </p>\n": "<p>\u521b\u5efa\u4f18\u5316\u5668</p>\n",
 "<p>Crop the image to the size of the mask </p>\n": "<p>\u5c06\u56fe\u50cf\u88c1\u526a\u4e3a\u8499\u7248\u7684\u5927\u5c0f</p>\n",
 "<p>Crop the target mask to the size of the logits. Size of the logits will be smaller if we don&#x27;t use padding in convolutional layers in the U-Net. </p>\n": "<p>\u5c06\u76ee\u6807\u8499\u7248\u88c1\u526a\u4e3a logits \u7684\u5927\u5c0f\u3002\u5982\u679c\u6211\u4eec\u4e0d\u5728 U-Net \u7684\u5377\u79ef\u5c42\u4e2d\u4f7f\u7528\u586b\u5145\uff0clogits \u7684\u5927\u5c0f\u4f1a\u53d8\u5c0f\u3002</p>\n",
 "<p>Dataloader </p>\n": "<p>\u6570\u636e\u52a0\u8f7d\u5668</p>\n",
 "<p>Dataset </p>\n": "<p>\u6570\u636e\u96c6</p>\n",
 "<p>Device to train the model on. <a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.device.DeviceConfigs\"><span translate=no>_^_0_^_</span></a>  picks up an available CUDA device or defaults to CPU. </p>\n": "<p>\u7528\u4e8e\u8bad\u7ec3\u6a21\u578b\u7684\u8bbe\u5907\u3002<a href=\"https://docs.labml.ai/api/helpers.html#labml_helpers.device.DeviceConfigs\"><span translate=no>_^_0_^_</span></a>\u9009\u62e9\u53ef\u7528\u7684 CUDA \u8bbe\u5907\u6216\u9ed8\u8ba4\u4e3a CPU\u3002</p>\n",
 "<p>Get a random sample </p>\n": "<p>\u968f\u673a\u83b7\u53d6\u6837\u672c</p>\n",
 "<p>Get predicted mask </p>\n": "<p>\u83b7\u53d6\u9884\u6d4b\u7684\u53e3\u7f69</p>\n",
 "<p>Get predicted mask logits </p>\n": "<p>\u83b7\u53d6\u9884\u6d4b\u7684\u63a9\u7801\u65e5\u5fd7</p>\n",
 "<p>Image logging </p>\n": "<p>\u56fe\u50cf\u65e5\u5fd7\u8bb0\u5f55</p>\n",
 "<p>Increment global step </p>\n": "<p>\u9012\u589e\u5168\u5c40\u6b65\u957f</p>\n",
 "<p>Initialize </p>\n": "<p>\u521d\u59cb\u5316</p>\n",
 "<p>Initialize the <a href=\"carvana.html\">Carvana dataset</a> </p>\n": "<p>\u521d\u59cb\u5316 C <a href=\"carvana.html\">arvana \u6570\u636e\u96c6</a></p>\n",
 "<p>Initialize the model </p>\n": "<p>\u521d\u59cb\u5316\u6a21\u578b</p>\n",
 "<p>Iterate through the dataset. Use <a href=\"https://docs.labml.ai/api/monit.html#labml.monit.mix\"><span translate=no>_^_0_^_</span></a> to sample <span translate=no>_^_1_^_</span> times per epoch. </p>\n": "<p>\u904d\u5386\u6570\u636e\u96c6\u3002\u7528\u4e8e<a href=\"https://docs.labml.ai/api/monit.html#labml.monit.mix\"><span translate=no>_^_0_^_</span></a>\u5bf9\u6bcf\u4e2a\u7eaa\u5143\u7684\u91c7\u6837<span translate=no>_^_1_^_</span>\u6b21\u6570\u3002</p>\n",
 "<p>Learning rate </p>\n": "<p>\u5b66\u4e60\u7387</p>\n",
 "<p>Log samples </p>\n": "<p>\u65e5\u5fd7\u6837\u672c</p>\n",
 "<p>Loss function </p>\n": "<p>\u4e8f\u635f\u51fd\u6570</p>\n",
 "<p>Make the gradients zero </p>\n": "<p>\u5c06\u6e10\u53d8\u8bbe\u4e3a\u96f6</p>\n",
 "<p>Move data to device </p>\n": "<p>\u5c06\u6570\u636e\u79fb\u52a8\u5230\u8bbe\u5907</p>\n",
 "<p>New line in the console </p>\n": "<p>\u63a7\u5236\u53f0\u4e2d\u7684\u65b0\u884c</p>\n",
 "<p>Number of channels in the image. <span translate=no>_^_0_^_</span> for RGB. </p>\n": "<p>\u56fe\u50cf\u4e2d\u7684\u901a\u9053\u6570\u3002<span translate=no>_^_0_^_</span>\u5bf9\u4e8e RGB\u3002</p>\n",
 "<p>Number of channels in the output mask. <span translate=no>_^_0_^_</span> for binary mask. </p>\n": "<p>\u8f93\u51fa\u63a9\u7801\u4e2d\u7684\u58f0\u9053\u6570\u3002<span translate=no>_^_0_^_</span>\u7528\u4e8e\u4e8c\u8fdb\u5236\u63a9\u7801\u3002</p>\n",
 "<p>Number of training epochs </p>\n": "<p>\u8bad\u7ec3\u5468\u671f\u7684\u6570\u91cf</p>\n",
 "<p>Save the model </p>\n": "<p>\u4fdd\u5b58\u6a21\u578b</p>\n",
 "<p>Set configurations. You can override the defaults by passing the values in the dictionary. </p>\n": "<p>\u8bbe\u7f6e\u914d\u7f6e\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u5728\u5b57\u5178\u4e2d\u4f20\u9012\u503c\u6765\u8986\u76d6\u9ed8\u8ba4\u503c\u3002</p>\n",
 "<p>Set models for saving and loading </p>\n": "<p>\u8bbe\u7f6e\u7528\u4e8e\u4fdd\u5b58\u548c\u52a0\u8f7d\u7684\u6a21\u578b</p>\n",
 "<p>Sigmoid function for binary classification </p>\n": "<p>\u4e8c\u8fdb\u5236\u5206\u7c7b\u7684 Sigmoid \u51fd\u6570</p>\n",
 "<p>Start and run the training loop </p>\n": "<p>\u542f\u52a8\u5e76\u8fd0\u884c\u8bad\u7ec3\u5faa\u73af</p>\n",
 "<p>Take an optimization step </p>\n": "<p>\u91c7\u53d6\u4f18\u5316\u6b65\u9aa4</p>\n",
 "<p>Track the loss </p>\n": "<p>\u8ffd\u8e2a\u635f\u5931</p>\n",
 "<p>Train the model </p>\n": "<p>\u8bad\u7ec3\u6a21\u578b</p>\n",
 "Code for training a U-Net model on Carvana dataset.": "\u7528\u4e8e\u5728 Carvana \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 U-Net \u6a21\u578b\u7684\u4ee3\u7801\u3002",
 "Training a U-Net on Carvana dataset": "\u5728 Carvana \u6570\u636e\u96c6\u4e0a\u8bad\u7ec3 U-Net"
}