srch

Model Description. Wide Residual networks simply have increased number of channels compared to ResNet. Otherwise the architecture is the same. Deeper ImageNet models with bottleneck block have increased number of channels in …

For each image in the training process, standard data augmentation, the searched policy and Cutout are applied in sequence. For Wide-ResNet-28-. 10, the step ...

Wide-Resnet-28-10 Tensorflow implementation. The code achieves about 95.56% accuracy in 120 epochs on CIFAR10 dataset, which is similar to the original ...

Wide Residual networks simply have increased number of channels compared to ResNet. Otherwise the architecture is the same. Deeper ImageNet models with ...

Jun 24, 2018 · WRN-28-8. The WRN-28-10 model could not be used due to GPU memory constraints, hence WRN-28-8 model was used instead with a batch size of 64. Each epoch requires roughly 886 seconds, and therefore this was only run for 100 epochs. It achieves a score of 95.08 %, less than the best score of 95.83 % obtained by the WRN-28-10 network.

To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we ...

09.01.2020 · Also, wide WRN-28–10 outperforms thin ResNet-1001 by 0.92% (with the same mini-batch size during training) on CIFAR-10 and 3.46% on CIFAR-100, …

01.12.2018 · WRN-16-8 & WRN-28-10: Shallower than and wider than WRN-40–4, and got even lower error rate. With shallower network, training time can be shorter since parallel computations are performed on GPUs no matter how wide. And it is the first paper to obtain lower than 20% for CIFAR-100 without any strong data augmentation!!! 3.2. Dropout

Problems on Residual Network (ResNet); WRNs (Wide Residual Networks); Results ... WRN-16–10 and WRN-28–10: The training time is much lower than the ...

Resnet

Model Description. Wide Residual networks simply have increased number of channels compared to ResNet. 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 ...

Download Table | Top-1 error rate comparison by ResNet 110, Wide ResNet 28-10 and ResNext 29-8-64 on CIFAR-10 and CIFAR-100. * indicates results by us ...

(a) Accuracy of Wide-ResNet-28-2, Wide-ResNet-28-7, and Wide-ResNet-28-10 across varying distortion magnitudes. Models are trained for 200 epochs on 45K training set examples. Squares indicate the distortion magnitude that achieves the maximal accuracy. (b) Optimal distortion magnitude across 7 Wide-ResNet-28 architectures with

Aug 10, 2020 · 此外，wide WRN-28-10在CIFAR-10上的性能优于thin ResNet-1001 0.92%（在训练期间具有相同的小批量大小），而在CIFAR-100上，wide WRN-28-10比thin ResNet-1001高出3.46%，层数少36倍（见表5）。

Table 4: Test error (%) of various wide networks on CIFAR-10 and CIFAR-100 ... Faster WRN-50-2-bottleneck outperforms ResNet-152 having 3 times less layers, ...

a 是最基本的ResNet结构，b 是用了bottleneck（瓶颈）的ResNet结构；. d 是在最基本的ResNet结构上加入dropout层的WideResNet结构。. 论文实验测试. 在cifar10和cifar100上，左图没用dropout的比较，它效果最好。. 右图是用dropout的比较，深度28宽度10时效果最好。. 左图上面的没用 ...