CIFAR-100 training on Resnet-18 downloaded from torchvision.models

I am trying to train a resnet-18 downloaded from torchvision model downloaded using the following command
model=torchvision.models.resnet18(pretrained=False, num_classes=100)

I am only able to reach an accuracy of 58%. I am using data-augmentations and hyperparameters followed by a lot of projects at github which locally specify the structure of the network instead of using the one from torchvision. I understand that the difference can arise from a lot of reasons.

What I am looking for is to know if there’s a project/set of hyperparameters which I can follow to perform well on the torchvision models. I have observed a similar trend for vgg16_bn as well.

Hi

@paganpasta did you solve the issue ? I have exactly the same results than you on cifar100.

Sadly no!

However, the difference arises from the reduced number of parameters in the locally defined models by these repositories.