Hi Wan,
Thanks for your great work. I am running your code with command
python3 network/run_engine.py --initial_model ./pretrained/synthetic.pth --mode Train --model_dir ./output --tag self-supervised --lr 0.0001
I've noticed this triggers joint training of real and synthetic dataset. I have this problem that during each epoch, the metric keeps going back and forth and is basically over 40mm. Here's the log example during training:
[12-2900]: metric: avg_joint_error: 60.2542 , loss: synt_uv: 15.4588 synt_d: 26.9756 mv_projection: 9740.4551 mv_consistency: 0.4032 uv_hm_mean: 0.0002 pose_prior: 0.6047 collision: 4.4668 bone_length: 122.4030 domain_loss: 0.0000 , lr: 0.0001, time: 39.56s
Did I do someting wrong or miss anything? It would be help a lot if you could help me figure it out!
Best wishes
Hi Wan,
Thanks for your great work. I am running your code with command
python3 network/run_engine.py --initial_model ./pretrained/synthetic.pth --mode Train --model_dir ./output --tag self-supervised --lr 0.0001I've noticed this triggers joint training of real and synthetic dataset. I have this problem that during each epoch, the metric keeps going back and forth and is basically over 40mm. Here's the log example during training:
Did I do someting wrong or miss anything? It would be help a lot if you could help me figure it out!
Best wishes