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docs(benchmarks): resnset custom/pytorch 测试
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zjykzj committed Nov 27, 2020
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# 基准测试

## ResNet

<table>
<thead>
<tr>
<th>config</th>
<th>backbone</th>
<th>pretrain</th>
<th>custom</th>
<th>gpus</th>
<th>batchs</th>
<th>top1 acc</th>
<th>top5 acc</th>
<th>resolution(TxHxW)</th>
<th>inference_time(image/s)</th>
<th>gpu_mem(M)</th>
<th>ckpt</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://cloud.zhujian.tech:9300/s/CL58YL7S7wHPTyr" target="_blank" rel="noopener noreferrer">r50_custom_cifar100_224</a></td>
<td>resnet50</td>
<td>from scratch</td>
<td>custom</td>
<td>1</td>
<td>96</td>
<td>39.504</td>
<td>67.659</td>
<td>3x224x224</td>
<td>/</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td><a href="https://cloud.zhujian.tech:9300/s/4ADbnYzsgk2SQfi" target="_blank" rel="noopener noreferrer">r50_custom_pretrained_cifar100_224</a></td>
<td>resnet50</td>
<td>torchvision pretrained</td>
<td>custom</td>
<td>1</td>
<td>96</td>
<td>82.183</td>
<td>97.321</td>
<td>3x224x224</td>
<td>/</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td><a href="https://cloud.zhujian.tech:9300/s/ZfHkTxSqce4zCwB" target="_blank" rel="noopener noreferrer">r50_pytorch_cifar100_224</a></td>
<td>resnet50</td>
<td>from scratch</td>
<td>torchvision</td>
<td>1</td>
<td>96</td>
<td>41.032</td>
<td>69.018</td>
<td>3x224x224</td>
<td>/</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td><a href="https://cloud.zhujian.tech:9300/s/k4aqzLAnqtXCM8X" target="_blank" rel="noopener noreferrer">r50_pytorch_pretrained_cifar100_224</a></td>
<td>resnet50</td>
<td>torchvision pretrained</td>
<td>torchvision</td>
<td>1</td>
<td>96</td>
<td>82.183</td>
<td>97.321</td>
<td>3x224x224</td>
<td>/</td>
<td>/</td>
<td>/</td>
</tr>
</tbody>
</table>

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