How can I understand which layers are frozen fine-tuning a detection model from Tensorflow Model Zoo 2? I have already set with success the Path for fine_tune_checkpoint and fine_tune_checkpoint_type: detection and in the file proto I have already read that "detection" means

// 2. "detection": Restores the entire feature extractor.
The only parts of the full detection model that are not restored are the box and class prediction heads. 
This option is typically used when you want to use a pre-trained detection model 
and train on a new dataset or task which requires different box and class prediction heads.

I didn't really understand what does that means. Restored means Frozen in this context?

CodePudding user response：

As I understand it, currently the Tensorflow 2 Object detection does not freeze any layers when training from a fine tune checkpoint. There is a issue reported here to support specifying which layers to freeze in the pipeline config. If you look at the training step function, you can see that all trainable variables are used when applying gradients during training.

Restored here means that the model weights are copied from the checkpoint to be used as a starting point for training. Frozen would mean that the weights are not changed (i.e. no gradient is applied) during training.