is there any code/tutorials to show how to use the pretrained pytorch models and test the evaluation performance for image classification on Imagenet and cifar10 100? (just testing not training, since I dont have the hardware for training)
For imagenet I am facing an inconsistency issue and I cannot find the details for the labels that are used to train the pytorch models.
In other words:
I notice that for pretrain models that are provided the labels are not consistent.
For example, vgg16 class 1 is different from Resnet50 class 1.
Can you let us know where we can find the corresponding labels for each model?
For vgg i notice that the one that looks like this:
"0": [
"n01440764",
"tench"
],
"1": [
"n01443537",
"goldfish"
],
"2": [
"n01484850",
"great_white_shark"
],
"3": [
"n01491361",
"tiger_shark"
],
"4": [
"n01494475",
"hammerhead"
],
"5": [
"n01496331",
"electric_ray"
],
"6": [
"n01498041",
"stingray"
],
"7": [
"n01514668",
"cock"
],
"8": [
"n01514859",
"hen"
works, but this one is not the one that we should use for resnets, please let us know what we should do.
Thanks