Comments (4)
About 48h on a low end GPU (2080). Maybe you're running on CPU, or have wrong hyperparams
from 4d-facial-avatars.
About 48h on a low end GPU (2080). Maybe you're running on CPU, or have wrong hyperparams
I took your code and ran it directly without modifying the parameters, and my graphics card capacity is slightly larger than the 2080. Just tried running the code again, and the console shows that it is expected to take over 500 hours to run. Below are screenshots of my graphics card parameters and code running.
from 4d-facial-avatars.
Hi, Have you found the reason for the slow training speed?
from 4d-facial-avatars.
Hi, Have you found the reason for the slow training speed?
Just reduce the number of iterations, for example, to 200000
from 4d-facial-avatars.
Related Issues (20)
- Question about the coordinate system
- Which ".yml" file is used to build the environment?
- index out of bounds HOT 1
- How to change the pose and expression during evaluation? HOT 1
- IndexError HOT 1
- for customized data preprocessing, how to generate the required shape expression vector?
- which repo you used in quantitative evaluation for DVP(Deep Video Portrait)? HOT 2
- How much RAM required for training? HOT 1
- Qualitative results different from paper: Blurry shoulders HOT 1
- Question about Test speed HOT 3
- Could this project be trained on multi-gpus? HOT 1
- Huge Artifacts occured in cross-id face reenactment results HOT 4
- about model of nerf
- How to accelerate the training process
- About index_map.npy HOT 1
- Why am I always unable to converge when running this code, and the PSNR has always been between 6 and 7 without any improvement
- I would like to ask about the training_ Transformed_ Is line 247 of rays.py written incorrectly, and should change the model_ coarse. train() to model_ fine. train()
- Why does the loss of late code increase during training
- Question: does this generate 3D volumes/meshes of the subject?
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