Open LiuJF1226 opened 10 months ago
First is model_dim
, it is set to 64 in resnet_320x1024.txt
and resnet_192x640.txt
, but it's 32 in args_res50_kitti_192x640_train.txt
, 56 in args_cityscapes_train.txt
.
Then is min_depth
and max_depth
. In most config files, they are set to 0.001 and 80. But max_depth
is set to 10 in args_cityscapes_train.txt
and max_depth
is set to 0.01 in resnet_320x1024.txt
.
And also, query_nums
, dim_out
and so on have different settings
I wonder how these hyper-parameters can influence the final performance. Now I want to train on Cityscapes from scratch, should I follow the same setting in args_cityscapes_train.txt
(max_depth=10
is somewhat strange to me, why not 80?)
max_depth
does not influence the scores (Because self-supervised training doesn't require absolute depth scale, and in evaluation, median-scaling is used for align depth_gts and depth predictions), but min_depth
does.
You can change model_dim
, query_nums
, dim_out
if you like, but these hyper-parameters can influence the scores, model parameters, model speed, etc.
Thank you for your reply. Actually I think the influence of min_depth
is also slight if it is small enough, i.e., setting it to 0.1, 0.01 or 0.001 may have a slight gap.
A nice work! but I am confused about some hyper-parameters.