use weight transfer learning trained in customer data after training in another customer data

Question

I hope you are well I have a question can I train transfer learning like pre-trained model 'vgg16' trained on ImageNet in my customer data and save weight to train another customer data? How I can do this, please Thanks for ur time

Answer 1

I do example with Pytorch framework.
When you train any model, you have to define training strategy.
There are two ways to save and load trained weight for transfer learning in Pytorch.

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The first is load state dict - only save and load the weight (paramemters) (recommended).
While training, if condition you defined satisfied, let's save the trained weight by this command (refer link)

torch.save(model.state_dict(), PATH)

And then when you train VGG16 model with other custom dataset and want to transfer learning from previous trained weight, use this

model = VGG16(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()

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The second way is load the whole model (model structure included)
To save model, use (refer link)

torch.save(model, PATH)

And to load it

# Model class must be defined somewhere
model = torch.load(PATH)
model.eval()

You can refer some links [1], [2]