Object detection fine-tuning - Select which layers are modified

Hi, I have a question about the procedure for fine-tuning a pre-trained object detection model.

As far as I understand, the described procedure modifies all the weight values in the model.
I wanted to only fine-tune the fully connected layer at the end of the network, and freeze the rest of the weights.

I assume that I should specify which parameters I want to modify when creating the Trainer:

	trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001, 'wd': 0.0005, 'momentum': 0.9})

so, instead of net.collect_params(), I should list the parameters I’m interested in training, and run the rest of the process normally.
However, I don’t know how to isolate these parameters precisely…I tried printing:

params = net.collect_params()

but, out of this list, I don’t know which ones correspond to the final FC layers. Any suggestions?