Hi,
I have some difficulties to understand when to use resume training or pretrained models.
Imagine I have already trained my model on some data (everyday position) to do Human Body coordinates Detections. And one day, I want to train it on some new data (in sports position) and for the same task in order to learn more from sport position.
Can I just consider my model as pretrained model and just do:
pre_net = torch.load(PATH_MODEL, map_location=device)
net.load_state_dict(pre_net)

for param in list(net.parameters()):
     param.requires_grad = True

And then train the model on new data (without loosing what the model has already lerned) ???
OR
Train my model from 0, Save it the right way (model, optimizer, loss, epoch …) and perform Resume Training?
Thank you!