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Explicit conversion: add link #311

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2 changes: 1 addition & 1 deletion pytorch_vision_wide_resnet.md
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Expand Up @@ -91,7 +91,7 @@ Otherwise the architecture is the same. Deeper ImageNet models with bottleneck
block have increased number of channels in the inner 3x3 convolution.

The `wide_resnet50_2` and `wide_resnet101_2` models were trained in FP16 with
mixed precision training using SGD with warm restarts. Checkpoints have weights in
mixed precision training using [SGD with warm restarts(SGDR)](https://arxiv.org/abs/1608.03983). Checkpoints have weights in
half precision (except batch norm) for smaller size, and can be used in FP32 models too.

| Model structure | Top-1 error | Top-5 error | # parameters |
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