Comments (2)
Thanks for the kind words.
To use Deep SVDD on your custom image datasets, you would have to write a DataLoader sub-class that specifies how the data can be loaded and should be pre-processed (.load_data method). Moreover, you must specify a neural network architecture you would like to apply for your data (. build_architecture method and .build_autoencoder method if you would like to pretrain via autoencoder, which I recommend).
Have a look at mnist.py and cifar10.py in the src/datasets directory to see how this is implemented for MNIST and CIFAR-10.
Also, I will release a cleaner and simpler PyTorch implementation of the method soon for which it will be easier to use implement your own datasets.
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Hello, I am using your code for my dataset. I have a few queries -
- My training data is labelled while my testing data is not. How should I go about implementing your code? What changes should I make?
- Regarding the data loader, I am confused about the syntax. Do I load my data through a .csv file or provide a path to my image directories? I have been unable to find decent methods to implement the data loading.
- For my case, do I have to edit the base directory modules in any way? I realize that I don't have to edit the optim and the util modules.
Thank you.
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Related Issues (14)
- AssertionError: AbstractConv2d Theano optimization failed: HOT 1
- GTSRB dataset‘s adversarial example
- Why I got two different results when all parameters are same? the results can not be reproduced.
- Training epchs
- How to run the mainpy?
- result for MNIST
- Looking forward to see the tensorflow version HOT 2
- Possible hard margin R bug? HOT 1
- Why is my experimental cifar10 result far worse than your paper shows? HOT 1
- STOP sign dataset HOT 3
- Could you please explain how to do prediction using One-Class Deep SVDD? HOT 4
- Hi, Sir! When will the Tensorflow code be released? HOT 1
- How to run soft-boundary Deep SVDD?
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