pytorch_HMR
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MandyMo
About training details and performance
Hello @MandyMo , Thank you for releasing the code, it is a really great work. I trained with this code and got reasonable results on 3d datasets, but terrible results on 2d datasets. The losses all seem stable. Then I tried to change hyperparameters, but the results on 2d datasets were far from good. I don't know whether it's normal or not. Really confused. How is the performance of your pre-trained model? What's your performance on training set and testing set of 2d datasets? Can you share the hyperparameter setting or give some instructions? Thanks a lot! Following are my training details and results.
This is the default parameters setting from this code, just changed the batch_size_(2d, 3d, adv) to fulfill my GPUs. I trained 7M samples for each dataset, you can regard it as about 1M iterations with batch size of 7.
Losses:
Training results from 3d datasets, they seem reasonable.
Training results from 2d datasets, they seem terrible, but the loss for 2d points keeps stable.
Then I amplified the ratio of 2d loss for 10 times, trying to lower 2d loss. But the result didn't goes the way I wanted. The 2d loss went a little bit lower if it divides 10 comparing the former one, it still much higher than 3d keypoint loss. And the results on 2d datasets were not satisfying.
The losses:
Training results from 3d datasets:
Training results from 2d datasets:
@willie1997 @hsyntemiz @mehameha998 I read the histories of issues, how about your training results? Thanks a lot.
@zhaoyang10 ok,,,,i think the model is for 2d input images ,, i did not read this work completely,,but, my understanding is this
You can send me e-mails. It's not convenient to release my wechat directly.
Hello @MandyMo , Thank you for releasing the code, it is a really great work. I trained with this code and got reasonable results on 3d datasets, but terrible results on 2d datasets. The losses all seem stable. Then I tried to change hyperparameters, but the results on 2d datasets were far from good. I don't know whether it's normal or not. Really confused. How is the performance of your pre-trained model? What's your performance on training set and testing set of 2d datasets? Can you share the hyperparameter setting or give some instructions? Thanks a lot! Following are my training details and results.
This is the default parameters setting from this code, just changed the batch_size_(2d, 3d, adv) to fulfill my GPUs. I trained 7M samples for each dataset, you can regard it as about 1M iterations with batch size of 7. Losses:
Then I amplified the ratio of 2d loss for 10 times, trying to lower 2d loss. But the result didn't goes the way I wanted. The 2d loss went a little bit lower if it divides 10 comparing the former one, it still much higher than 3d keypoint loss. And the results on 2d datasets were not satisfying. The losses:
Training results from 3d datasets:
do you have the training datasets?
