chore: import upstream snapshot with attribution
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wehub-resource-sync
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"<h1>Train a <a href=\"index.html\">ResNet</a> on CIFAR 10</h1>\n": "<h1>CIFAR <a href=\"index.html\">10\u3067\u30ea\u30cd\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u3088\u3046</a></h1>\n",
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"<h2>Configurations</h2>\n<p>We use <a href=\"../experiments/cifar10.html\"><span translate=no>_^_0_^_</span></a> which defines all the dataset related configurations, optimizer, and a training loop.</p>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u95a2\u9023\u3059\u308b\u3059\u3079\u3066\u306e\u69cb\u6210\u3001\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7\u3092\u5b9a\u7fa9\u3059\u308b\u3082\u306e\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059<a href=\"../experiments/cifar10.html\"><span translate=no>_^_0_^_</span></a>\u3002</p>\n",
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"<h3>Create model</h3>\n": "<h3>\u30e2\u30c7\u30eb\u4f5c\u6210</h3>\n",
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"<p> </p>\n": "<p></p>\n",
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"<p><a href=\"index.html\">ResNet</a> </p>\n": "<p><a href=\"index.html\">ResNet</a></p>\n",
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"<p>Bottleneck sizes </p>\n": "<p>\u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u30b5\u30a4\u30ba</p>\n",
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"<p>Create configurations </p>\n": "<p>\u69cb\u6210\u306e\u4f5c\u6210</p>\n",
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"<p>Create experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u4f5c\u6210</p>\n",
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"<p>Kernel size of the initial convolution layer </p>\n": "<p>\u521d\u671f\u7573\u307f\u8fbc\u307f\u5c64\u306e\u30ab\u30fc\u30cd\u30eb\u30b5\u30a4\u30ba</p>\n",
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"<p>Linear layer for classification </p>\n": "<p>\u5206\u985e\u7528\u306e\u7dda\u5f62\u30ec\u30a4\u30e4\u30fc</p>\n",
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"<p>Load configurations </p>\n": "<p>\u69cb\u6210\u3092\u30ed\u30fc\u30c9</p>\n",
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"<p>Move the model to the device </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
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"<p>Number fo blocks for each feature map size </p>\n": "<p>\u5404\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u30b5\u30a4\u30ba\u306e\u30d6\u30ed\u30c3\u30af\u6570</p>\n",
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"<p>Number of channels for each feature map size </p>\n": "<p>\u5404\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u30b5\u30a4\u30ba\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</p>\n",
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"<p>Set model for saving/loading </p>\n": "<p>\u4fdd\u5b58/\u8aad\u307f\u8fbc\u307f\u7528\u306e\u30e2\u30c7\u30eb\u3092\u8a2d\u5b9a</p>\n",
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"<p>Stack them </p>\n": "<p>\u7a4d\u307f\u91cd\u306d\u3066</p>\n",
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"<p>Start the experiment and run the training loop </p>\n": "<p>\u5b9f\u9a13\u3092\u958b\u59cb\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7\u3092\u5b9f\u884c\u3057\u307e\u3059</p>\n",
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"Train a ResNet on CIFAR 10": "CIFAR 10\u3067\u30ea\u30cd\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u3088\u3046"
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}
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{
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"<h1>Train a <a href=\"index.html\">ResNet</a> on CIFAR 10</h1>\n<p><a href=\"https://app.labml.ai/run/fc5ad600e4af11ebbafd23b8665193c1\"><span translate=no>_^_0_^_</span></a></p>\n": "<h1>CIFAR10 \u0db8\u0dad <a href=\"index.html\">\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca</a> \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4</h1>\n<p><a href=\"https://app.labml.ai/run/fc5ad600e4af11ebbafd23b8665193c1\"><span translate=no>_^_0_^_</span></a></p>\n",
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"<h2>Configurations</h2>\n<p>We use <a href=\"../experiments/cifar10.html\"><span translate=no>_^_0_^_</span></a> which defines all the dataset related configurations, optimizer, and a training loop.</p>\n": "<h2>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dca</h2>\n<p>\u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8\u0daf\u0dad\u0dca\u0dad \u0d9a\u0da7\u0dca\u0da7\u0dbd \u0d86\u0dc1\u0dca\u0dbb\u0dd2\u0dad \u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca, \u0db4\u0dca\u0dbb\u0dc1\u0dc3\u0dca\u0dad\u0d9a\u0dbb\u0dab\u0dba \u0dc3\u0dc4 \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0dbd\u0dd6\u0db4\u0dba\u0d9a\u0dca \u0db1\u0dd2\u0dbb\u0dca\u0dc0\u0da0\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0d85\u0db4\u0dd2 \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf <a href=\"../experiments/cifar10.html\"><span translate=no>_^_0_^_</span></a> \u0d9a\u0dbb\u0db8\u0dd4. </p>\n",
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"<h3>Create model</h3>\n": "<h3>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</h3>\n",
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"<p> </p>\n": "<p> </p>\n",
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"<p><a href=\"index.html\">ResNet</a> </p>\n": "<p><a href=\"index.html\">\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca</a> </p>\n",
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"<p>Bottleneck sizes </p>\n": "<p>\u0db6\u0dcf\u0db0\u0d9a\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab </p>\n",
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"<p>Create configurations </p>\n": "<p>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca\u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1 </p>\n",
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"<p>Create experiment </p>\n": "<p>\u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf\u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1 </p>\n",
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"<p>Kernel size of the initial convolution layer </p>\n": "<p>\u0d86\u0dbb\u0db8\u0dca\u0db7\u0d9a\u0dc3\u0d82\u0dc0\u0dbd\u0dd2\u0dad \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0dda \u0d9a\u0dbb\u0dca\u0db1\u0dbd\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba </p>\n",
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"<p>Linear layer for classification </p>\n": "<p>\u0dc0\u0dbb\u0dca\u0d9c\u0dd3\u0d9a\u0dbb\u0dab\u0dba\u0dc3\u0db3\u0dc4\u0dcf \u0dbb\u0dda\u0d9b\u0dd3\u0dba \u0dc3\u0dca\u0dae\u0dbb\u0dba </p>\n",
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"<p>Load configurations </p>\n": "<p>\u0dc0\u0dd2\u0db1\u0dca\u0dba\u0dcf\u0dc3\u0dba\u0db1\u0dca\u0db4\u0dd6\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
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"<p>Move the model to the device </p>\n": "<p>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba\u0dc0\u0dd9\u0dad \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0d9c\u0dd9\u0db1\u0dba\u0db1\u0dca\u0db1 </p>\n",
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"<p>Number fo blocks for each feature map size </p>\n": "<p>\u0d91\u0d9a\u0dca\u0d91\u0d9a\u0dca \u0dbd\u0d9a\u0dca\u0dc2\u0dab\u0dba \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2 \u0daf\u0dd0\u0db8\u0dd2\u0db8\u0dda \u0d85\u0d82\u0d9a\u0dba </p>\n",
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"<p>Number of channels for each feature map size </p>\n": "<p>\u0d91\u0d9a\u0dca\u0d91\u0d9a\u0dca \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 </p>\n",
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"<p>Set model for saving/loading </p>\n": "<p>\u0d89\u0dad\u0dd2\u0dbb\u0dd2\u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db4\u0dd0\u0da7\u0dc0\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1 </p>\n",
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"<p>Stack them </p>\n": "<p>\u0d92\u0dc0\u0dcf\u0d9c\u0ddc\u0da9\u0d9c\u0dc3\u0db1\u0dca\u0db1 </p>\n",
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"<p>Start the experiment and run the training loop </p>\n": "<p>\u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf\u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0dbd\u0dd6\u0db4\u0dba \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb\u0db1\u0dca\u0db1 </p>\n",
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"Train a ResNet on CIFAR 10": "CIFAR 10 \u0db8\u0dad \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4"
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}
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{
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"<h1>Train a <a href=\"index.html\">ResNet</a> on CIFAR 10</h1>\n": "<h1>\u5728 CIFA <a href=\"index.html\">R 10 \u4e0a\u8bad\u7ec3 ResNet</a></h1>\n",
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"<h2>Configurations</h2>\n<p>We use <a href=\"../experiments/cifar10.html\"><span translate=no>_^_0_^_</span></a> which defines all the dataset related configurations, optimizer, and a training loop.</p>\n": "<h2>\u914d\u7f6e</h2>\n<p>\u6211\u4eec\u4f7f\u7528<a href=\"../experiments/cifar10.html\"><span translate=no>_^_0_^_</span></a>\u5b83\u6765\u5b9a\u4e49\u6240\u6709\u4e0e\u6570\u636e\u96c6\u76f8\u5173\u7684\u914d\u7f6e\u3001\u4f18\u5316\u5668\u548c\u8bad\u7ec3\u5faa\u73af\u3002</p>\n",
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"<h3>Create model</h3>\n": "<h3>\u521b\u5efa\u6a21\u578b</h3>\n",
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"<p> </p>\n": "<p></p>\n",
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"<p><a href=\"index.html\">ResNet</a> </p>\n": "<p><a href=\"index.html\">ResNet</a></p>\n",
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"<p>Bottleneck sizes </p>\n": "<p>\u74f6\u9888\u5927\u5c0f</p>\n",
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"<p>Create configurations </p>\n": "<p>\u521b\u5efa\u914d\u7f6e</p>\n",
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"<p>Create experiment </p>\n": "<p>\u521b\u5efa\u5b9e\u9a8c</p>\n",
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"<p>Kernel size of the initial convolution layer </p>\n": "<p>\u521d\u59cb\u5377\u79ef\u5c42\u7684\u5185\u6838\u5927\u5c0f</p>\n",
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"<p>Linear layer for classification </p>\n": "<p>\u7528\u4e8e\u5206\u7c7b\u7684\u7ebf\u6027\u5c42</p>\n",
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"<p>Load configurations </p>\n": "<p>\u88c5\u8f7d\u914d\u7f6e</p>\n",
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"<p>Move the model to the device </p>\n": "<p>\u5c06\u6a21\u578b\u79fb\u5230\u8bbe\u5907\u4e0a</p>\n",
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"<p>Number fo blocks for each feature map size </p>\n": "<p>\u6bcf\u4e2a\u8981\u7d20\u5730\u56fe\u5927\u5c0f\u7684\u533a\u5757\u6570</p>\n",
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"<p>Number of channels for each feature map size </p>\n": "<p>\u6bcf\u4e2a\u8981\u7d20\u6620\u5c04\u5927\u5c0f\u7684\u901a\u9053\u6570</p>\n",
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"<p>Set model for saving/loading </p>\n": "<p>\u8bbe\u7f6e\u4fdd\u5b58/\u52a0\u8f7d\u7684\u6a21\u578b</p>\n",
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"<p>Stack them </p>\n": "<p>\u5806\u53e0\u5b83\u4eec</p>\n",
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"<p>Start the experiment and run the training loop </p>\n": "<p>\u5f00\u59cb\u5b9e\u9a8c\u5e76\u8fd0\u884c\u8bad\u7ec3\u5faa\u73af</p>\n",
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"Train a ResNet on CIFAR 10": "\u5728 CIFAR 10 \u4e0a\u8bad\u7ec3 ResNet"
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}
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{
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"<h1><a href=\"https://nn.labml.ai/resnet/index.html\">Deep Residual Learning for Image Recognition (ResNet)</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/1512.03385\">Deep Residual Learning for Image Recognition</a>.</p>\n<p>ResNets train layers as residual functions to overcome the <em>degradation problem</em>. The degradation problem is the accuracy of deep neural networks degrading when the number of layers becomes very high. The accuracy increases as the number of layers increase, then saturates, and then starts to degrade.</p>\n": "<h1><a href=\"https://nn.labml.ai/resnet/index.html\">\u753b\u50cf\u8a8d\u8b58\u306e\u305f\u3081\u306e\u6df1\u5c64\u6b8b\u5dee\u5b66\u7fd2 (ResNet)</a></h1>\n<p>\u3053\u308c\u306f\u3001\u300c<a href=\"https://arxiv.org/abs/1512.03385\">\u753b\u50cf\u8a8d\u8b58\u306e\u305f\u3081\u306e\u6df1\u5c64\u6b8b\u5dee\u5b66\u7fd2</a>\u300d<a href=\"https://pytorch.org\">\u3068\u3044\u3046\u8ad6\u6587\u3092PyTorch\u3067\u5b9f\u88c5\u3057\u305f\u3082\u306e\u3067\u3059</a>\u3002</p>\n<p><em>ResNet\u306f\u52a3\u5316\u306e\u554f\u984c\u3092\u514b\u670d\u3059\u308b\u305f\u3081\u306b\u5c64\u3092\u6b8b\u5dee\u95a2\u6570\u3068\u3057\u3066\u5b66\u7fd2\u3055\u305b\u307e\u3059</em>\u3002\u52a3\u5316\u306e\u554f\u984c\u306f\u3001\u5c64\u306e\u6570\u304c\u975e\u5e38\u306b\u591a\u304f\u306a\u308b\u3068\u3001\u30c7\u30a3\u30fc\u30d7\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u7cbe\u5ea6\u304c\u4f4e\u4e0b\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u30ec\u30a4\u30e4\u30fc\u306e\u6570\u304c\u5897\u3048\u308b\u3068\u7cbe\u5ea6\u304c\u4e0a\u304c\u308a\u3001\u98fd\u548c\u3057\u3001\u52a3\u5316\u304c\u59cb\u307e\u308a\u307e\u3059</p>\u3002\n",
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"Deep Residual Learning for Image Recognition (ResNet)": "\u753b\u50cf\u8a8d\u8b58\u306e\u305f\u3081\u306e\u6df1\u5c64\u6b8b\u5dee\u5b66\u7fd2 (ResNet)"
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}
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{
|
||||
"<h1><a href=\"https://nn.labml.ai/resnet/index.html\">Deep Residual Learning for Image Recognition (ResNet)</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/1512.03385\">Deep Residual Learning for Image Recognition</a>.</p>\n<p>ResNets train layers as residual functions to overcome the <em>degradation problem</em>. The degradation problem is the accuracy of deep neural networks degrading when the number of layers becomes very high. The accuracy increases as the number of layers increase, then saturates, and then starts to degrade.</p>\n": "<h1><a href=\"https://nn.labml.ai/resnet/index.html\">\u0dbb\u0dd6\u0db4 \u0dc4\u0db3\u0dd4\u0db1\u0dcf\u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8 (\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca)</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://pytorch.org\">PyTorch</a> \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 <a href=\"https://arxiv.org/abs/1512.03385\">\u0dbb\u0dd6\u0db4 \u0dc4\u0db3\u0dd4\u0db1\u0dcf\u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> . </p>\n<p><em>\u0db4\u0dd2\u0dbb\u0dd2\u0dc4\u0dd3\u0db8\u0dda\u0d9c\u0dd0\u0da7\u0dc5\u0dd4\u0dc0</em>\u0db8\u0d9f\u0dc4\u0dbb\u0dc0\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba\u0db1\u0dca \u0dbd\u0dd9\u0dc3 \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca\u0dc3\u0dca \u0dc3\u0dca\u0dae\u0dbb \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2. \u0db4\u0dd2\u0dbb\u0dd2\u0dc4\u0dd3\u0db8\u0dda \u0d9c\u0dd0\u0da7\u0dc5\u0dd4\u0dc0 \u0dc0\u0db1\u0dca\u0db1\u0dda \u0dc3\u0dca\u0dae\u0dbb \u0d9c\u0dab\u0db1 \u0d89\u0dad\u0dcf \u0d89\u0dc4\u0dc5 \u0dc0\u0db1 \u0dc0\u0dd2\u0da7 \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc3\u0dca\u0db1\u0dcf\u0dba\u0dd4\u0d9a \u0da2\u0dcf\u0dbd \u0db4\u0dd2\u0dbb\u0dd2\u0dc4\u0dd3\u0db8\u0dda \u0db1\u0dd2\u0dbb\u0dc0\u0daf\u0dca\u0dba\u0dad\u0dcf\u0dc0\u0dba\u0dba\u0dd2. \u0dc3\u0dca\u0dae\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dd0\u0da9\u0dd2 \u0dc0\u0db1 \u0dc0\u0dd2\u0da7 \u0db1\u0dd2\u0dbb\u0dc0\u0daf\u0dca\u0dba\u0dad\u0dcf\u0dc0 \u0dc0\u0dd0\u0da9\u0dd2 \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0db4\u0dc3\u0dd4\u0dc0 \u0dc3\u0db1\u0dca\u0dad\u0dd8\u0db4\u0dca\u0dad \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0db4\u0dc3\u0dd4\u0dc0 \u0db4\u0dd2\u0dbb\u0dd2\u0dc4\u0dd3\u0db8\u0da7 \u0db4\u0da7\u0db1\u0dca \u0d9c\u0db1\u0dd3. </p>\n",
|
||||
"Deep Residual Learning for Image Recognition (ResNet)": "\u0dbb\u0dd6\u0db4 \u0dc4\u0db3\u0dd4\u0db1\u0dcf\u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8 (\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca)"
|
||||
}
|
||||
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"<h1><a href=\"https://nn.labml.ai/resnet/index.html\">Deep Residual Learning for Image Recognition (ResNet)</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of the paper <a href=\"https://arxiv.org/abs/1512.03385\">Deep Residual Learning for Image Recognition</a>.</p>\n<p>ResNets train layers as residual functions to overcome the <em>degradation problem</em>. The degradation problem is the accuracy of deep neural networks degrading when the number of layers becomes very high. The accuracy increases as the number of layers increase, then saturates, and then starts to degrade.</p>\n": "<h1><a href=\"https://nn.labml.ai/resnet/index.html\">\u7528\u4e8e\u56fe\u50cf\u8bc6\u522b\u7684\u6df1\u5ea6\u6b8b\u5dee\u5b66\u4e60 (ResNet)</a></h1>\n<p>\u8fd9\u662f\u8bba\u6587\u300a<a href=\"https://arxiv.org/abs/1512.03385\">\u7528\u4e8e\u56fe\u50cf\u8bc6\u522b\u7684\u6df1\u5ea6\u6b8b\u5dee\u5b66\u4e60</a>\u300b\u7684 <a href=\"https://pytorch.org\">PyTorch</a> \u5b9e\u73b0\u3002</p>\n<p>ResNets \u5c06\u5c42\u8bad\u7ec3\u4e3a\u6b8b\u4f59\u51fd\u6570\uff0c\u4ee5\u514b\u670d<em>\u964d\u7ea7\u95ee\u9898</em>\u3002\u9000\u5316\u95ee\u9898\u662f\uff0c\u5f53\u5c42\u6570\u53d8\u5f97\u975e\u5e38\u9ad8\u65f6\uff0c\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u7684\u51c6\u786e\u6027\u4f1a\u964d\u4f4e\u3002\u7cbe\u5ea6\u968f\u7740\u5c42\u6570\u7684\u589e\u52a0\u800c\u63d0\u9ad8\uff0c\u7136\u540e\u9971\u548c\uff0c\u7136\u540e\u5f00\u59cb\u964d\u4f4e\u3002</p>\n",
|
||||
"Deep Residual Learning for Image Recognition (ResNet)": "\u7528\u4e8e\u56fe\u50cf\u8bc6\u522b\u7684\u6df1\u5ea6\u6b8b\u5dee\u5b66\u4e60 (ResNet)"
|
||||
}
|
||||
Reference in New Issue
Block a user