Yes, I managed to load ResNets that I trained on CIFAR datasets. The code for that is:

model = wrn.WideResNet(depth=number_of_layers, num_classes=100, widen_factor=4)
checkpoint = torch.load(‘runs/WideResNet-28-10/cifar_10.pth.tar’)
model.load_state_dict(checkpoint[‘state_dict’])
model = model.cuda()

The parameters for the model and for the net you are loading should agree.
For what is worth, the accuracy I got was:

Cifar-10:     0.9548
Cifar-100:   0.7868​

with these hyperparameters:

layers: 40 convs
learning rate: 0.1
momentum: nesterov with 0.9 param
regularization: Tikhonov with 5e-4 param
widen_factor: 4
batch size: 128
number of epochs: 200

Would be interesting to see what happens if I use some more advanced optimizer like Adam.
Anyway, in case you don’t have time to train them, I can upload the models today, during the afternoon.