assert params.num_classes >= 1597, 'class number need to be larger than max label id in base class' about closerlookfewshot HOT 5 CLOSED

I have processed the omniglot into noLatin.json as the requirement, however, it raises this issue.

Traceback (most recent call last): File "train.py", line 108, in assert params.num_classes >= 1597, 'class number need to be larger than max label id in base class' AssertionError: class number need to be larger than max label id in base class

The form of Json is "{label_names": ["Alphabet_of_the_Magi/character01","Alphabet_of_the_Magi/character02",..."ULOG/character26"],"image_names": ["E:/Project/CloserLookFewShot-master/filelists/omniglot/images/Alphabet_of_the_Magi/character01/0709_19.png",...,"E:/Project/CloserLookFewShot-master/filelists/omniglot/images/ULOG/character26/1623_15.png"],"image_labels": [0,...,1596]}

When you train the model, you need to specify num_classes, for example python ./train.py --model Conv4 --method baseline --num_classes 1597

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I have processed the omniglot into noLatin.json as the requirement, however, it raises this issue.
Traceback (most recent call last): File "train.py", line 108, in assert params.num_classes >= 1597, 'class number need to be larger than max label id in base class' AssertionError: class number need to be larger than max label id in base class
The form of Json is "{label_names": ["Alphabet_of_the_Magi/character01","Alphabet_of_the_Magi/character02",..."ULOG/character26"],"image_names": ["E:/Project/CloserLookFewShot-master/filelists/omniglot/images/Alphabet_of_the_Magi/character01/0709_19.png",...,"E:/Project/CloserLookFewShot-master/filelists/omniglot/images/ULOG/character26/1623_15.png"],"image_labels": [0,...,1596]}

When you train the model, you need to specify num_classes, for example python ./train.py --model Conv4 --method baseline --num_classes 1597

Thank you for the helpful answer, however, I have added the --num_classes 1597 or defined the num_classes = 1597 before reporting this issue. I think there may have another reason.

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Thank you for the helpful answer, however, I have added the --num_classes 1597 or defined the unm_classes = 1597 before reporting this issue. I think there may have another reason.

Have you ever tried the cross domain tasks(miniImagenet-CUB) in table 3? I have met the problem of lower accuracy 52% in baseline using ResNet18. And I didn't get the reason of this. If you have done this research, can you tell me what the problem is? Thank you very much!

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Thank you for the helpful answer, however, I have added the --num_classes 1597 or defined the unm_classes = 1597 before reporting this issue. I think there may have another reason.

Have you ever tried the cross domain tasks(miniImagenet-CUB) in table 3? I have met the problem of lower accuracy 52% in baseline using ResNet18. And I didn't get the reason of this. If you have done this research, can you tell me what the problem is? Thank you very much!

Sorry, I do not have the imagenent dataset on my PC. Therefore, I tried cross_char instead but it can not run.

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Hello, were you able to resolve the issue of running cross_char? We were able to run cross_char with python ./train.py --dataset cross_char --method baseline++ --num_classes 1597, however the baseline++ accuracy (80%) is lower than baseline (85%) for omniglot -> EMNIST. Please let us know if you also have this issue, thank you so much.

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