wide-resnet.pytorch
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meliketoy
Best CIFAR-10, CIFAR-100 results with wide-residual networks using PyTorch
Mine: ```python dataset = datasets.CIFAR10(root='./data', download=True, train=True, transform=transforms.ToTensor()) mean = dataset.data.astype(float).mean(axis=(0,1,2)) / 255 std = dataset.data.astype(float).std(axis=(0,1,2)) / 255 print("cifar10", np.round(mean, 4), np.round(std, 4)) --> cifar10 [0.4914 0.4822 0.4465] [0.247 0.2435...
dear Author, I downloaded your code and reproduced your experiment on cifar-10 according to your settings, but got 95.24 % test accuracy with cross entropy loss. I am not sure...
Do I need to set the widen factor to 1? or 0? ```python main --lr 0.1 resume false --net_type [lenet/vggnet/resnet/wide-resnet] --depth 34 --widen_factor 1 --dropout_rate 0.3 --dataset cifar10``
Following from the model graph for wideresnet50 with depth 28 and widen_factor = 10, layer2.0.conv2 and layer3.0.conv2 have stride - `stride=(2, 2)`. It should be layer2.0.conv1 and layer3.0.conv1 that have...
@meliketoy 1. BatchNorm2d ``` self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9) ``` In the official Pytorch code, they use the default value of momentum, momentum=0.1. https://github.com/szagoruyko/wide-residual-networks/blob/ae6d0d0561484172790c7a63c8ce6ade5a5a2914/pytorch/utils.py#L55 I think you've confused with the momentum...
Hi, nice repo! I noticed the code does not work on imagenet. It is a input dimensionality problem. It would be very nice if you could fix it!
Hi, First of all, Thank you so much for sharing the code. Maybe because of my lack of knowledge, I wonder that is there any difference between declaring an optimizer...
Hi, After following the install instructions, when I try and run main.py on CIFAR10, on the first epoch I get the following error: `Traceback (most recent call last): File "../main.py",...
I get this issue with using the _wide_layer ``` 30 def make_layer(self, block, out_channels, n_blocks, dropout_rate, stride): ---> 31 strides = [stride] + [1]*(n_blocks-1) 32 layers = [] 33 TypeError:...
