Training config for hr_new (ienet2.py)
marcelpuyat opened this issue · 1 comments
The README says the following:
For the ienet2.py (hr_new in config) we take inspiration from superresolution works: we remove all batch/group normalization and initialize the residual paths such they have a lower initial contribution. Further, we add 2 stages to the HRNet. This further improves final quality as well as training stability.
The training config provided in train.yaml seems to only work for hr (ienet.py) – simply changing hr to hr_new didn't seem to work for me (generated images were really bad). Would you mind submitting the config used that gave the improved final quality and training stability? Thanks!
Nevermind – these were uploaded as separate yaml files already. Namely: train_pfd2cs_ie2.yaml