-
Notifications
You must be signed in to change notification settings - Fork 664
Using cifar-100 with 15 classes #203
Comments
You've missed an argument after -data parameter. Your command should looks like
|
Hi @onidzelskyi , Thanks for your answer, you're right of course but this isn't the problem - I just didn't specify the path to the data, but I was using it. Running the same command with cifar10 works ok. Thanks, Yotam |
Yes, you right - I've the same issue when trying to train with small #classes (3 classes in my case) - it gives the same error you experienced with. Seems, for big networks (cifar100 for your case and resent-200 in my case) #classes should be equals or more than some threshold value. |
The option -resetClassifier replaces the output layer of the original model with a new output layer with the -nClasses you provide. So, in your example it will create a new network with 15 output neurons, and you are training on cifar100 which has 100 classes. The assertion fails as the output and target should be the same size for the loss function to be evaluated. |
Fix me if I'm on wrong way.
But I get an error I have no idea how to adopt it to my own dataset |
You don't need to set the
|
gives an error
dataset directory structure
|
Hi,
I'm trying to classify my images to 15 classes, and use cifar-100 for that.
I'm using the following command -
th main.lua -data -nClasses 15 -resetClassifier true -dataset cifar100 -depth 22
and I get the following error -
Assertiont >= 0 && t < n_classes failed.
I don't get this with any of the other datasets (cifar-10, imagenet)
Any clue, someone?
Thanks! Yotam
The text was updated successfully, but these errors were encountered: