how to create pretrained weight using VGG16(pytorch) with custom datasets

Question

I just started to learn pytorch.

However, do you know the method of creating pretrained weight for SSD pytorch?

We have a custom dataset, so we want to create pretrained weight with the custom dataset using VGG16 to enhance the performance of SSD.

Then bring the weight from there will be used for SSD.

Let me know the feasibility of it.

Thank you in advance

Answer 1

Pretrained weights are acquired by training the neural network on a large dataset such as ImageNet in a classification task. It is common for libraries to provide an option to load the weights from such training (hence the name pre-trained model): for instance, models found in torchvision.models has a pretrained option.

If you have a custom dataset then you will have to train your model either from scratch using a randomly initialized model, or starting training from ImageNet weights which is usually the best option.