Transfer learning from the subset of the ImageNet classes

vision
1

Hi

Is it possible to get a pretrained model that is based on the selective classes of the ImageNet?

I have computed the similarity between each Imagenet class and my dataset using cvpr18-inaturalist-transfer/DomainSimilarityDemo.ipynb at master · richardaecn/cvpr18-inaturalist-transfer · GitHub

and got the subset of Imagenet Classes which are most similar to my dataset. Now I want to use the pretrained features only from these most similar classes.

Earlier, I was using the pretrained model as follows:

def get_resnet34(num_classes=20, **_):
    model_name = 'resnet34'
    model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
    conv1 = model.conv1

May I know how can i get the pretrained features from the selective classes of Imagenet.
I don’t have any clue to pursue this further.
Looking for help.

Thank you

2

Perhaps you can take the output (which is a list of probabilities) of resnet34, set the probabilities corresponding to the non-selected classes to zeroes, and normalize the remaining probabilities (scale them up by a uniform factor so that they again add up to 1)?

This seems to be the simplest interpretation of “drop some classes from resnet34”. Perhaps this will work for your purpose?

3

Thanks for the suggestion.

Actually, output of this resnet model is 20 classes from my data set. I want to drop few classes from 1000 classes of ImageNet.
Do you think if that is possible.

Thanks

4

I am not sure I understand. If the output of this resnet models already matches your data set, why do you want to drop some classes?

5

My data is highly imbalanced, and I am using pretrained ResNet model to get the logits.
In hope of getting better accuracy, i want to use only those classes of ImageNet which are similar to the classes of my dataset.
There is a paper on this approach : ‘Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning’.
But unfortunately code is not complete

6

Did you try my first suggestion? To me that looks like a reasonable first thing to try.

7

Thanks for the reply.
Sorry, i am not sure if i understood that correctly. Isn’t that the ‘output (which is a list of probabilities) of resnet34’ will be 20 as the number of my dataset classes are 20, while Imagenet classes are 1000.

8

How does resnet34 know that the number of your data classes is 20?

9

That is the input parameter here

10

I see. Your problem is much more basic than I thought. I suggest that you first do a course in Python to get a basic understanding of the language, before using it to play with neural networks.

11

I think, I have not been able to put up my problem in a very understandable way here. anyways thanks.