Comments (6)
And what is the main difference between resume_from
and finetune
configs in your code?
from featdepth.
Hi! I'm curious about pretrained weights and configs: which config was used to receive this and which for this. I guess it
cfg_kitti_fm.py
andcfg_kitti_fm_refine.py
respectively. Am I right?
Yes, you are right, the name of config corresponds with that of ckpt. Kitti refer to training data, fm refer to model, refine refer to online refinement.
from featdepth.
And what is the main difference between
resume_from
andfinetune
configs in your code?
The resume_from mode will reload pretrained weights and will continue the unfinished training.
If the training is finished, the resume_from mode will not continue training.
While finetune mode will training from the very beginning.
from featdepth.
And what is the main difference between
resume_from
andfinetune
configs in your code?The resume_from mode will reload pretrained weights and will continue the unfinished training.
If the training is finished, the resume_from mode will not continue training.
While finetune mode will training from the very beginning.
So if I want to train pretrained on kitti weights with cityscapes dataset, I have to use finetune
parameter? Or I can change total_epochs
and data
in config and set resume_from
?
from featdepth.
The two ways will work the same, but I suggest you to use the finetune mode.
from featdepth.
Okay. Thank you a lot!
from featdepth.
Related Issues (20)
- running infer_singleimage.py in win10 ModuleNotFoundError: No module named 'resource' HOT 1
- it seems that the decoder of the auto-encoder network is not trained in the code HOT 1
- About the training and testing setting. HOT 4
- Is there a way to visually check the output of the FeatureNet HOT 1
- Question about MS evaluation HOT 5
- dataset.flag HOT 1
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- Problems using DDP HOT 2
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- cfg_kitti_fm.py stops while training, expected 4 input channels, but got 3 channels instead
- train on own dataset with fm_joint.cfg HOT 1
- pose question HOT 1
- pose question HOT 2
- feature-metric loss only use the first output of the Autoencoder HOT 2
- Evaluation Issue HOT 1
- how to use multi-gpus training?
- Weights for Monocular-only training
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from featdepth.