Comments (6)
These are losses and training renders.
Meanwhile I tried several different hyperparamters, but it didn't work much.
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We did not use any special tricks. I can see from the plots that the camera scale and ty are the same in all images. I've seen this collapse happen a few times, and when this happens I restarted the training and it worked. Usually you can see if this is the case in the first few thousand iterations. Can you try that?
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Thanks for replying.
'Restart' means resuming or starting from epoch 1?
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Start training from scratch.
Can you try to finetune one of the pretrained models? If something is wrong with the data then the metrics should start to become worse.
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I use h36m and up3d to train the vertices part ( graph_cnn.gc + graph_cnn.shape ), it seems well.
Then fix the parameters, train the camera part ( graph_cnn.camera ) several times, it always collapses to around (0.85, 0.001, 0.2).
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Start training from scratch.
Can you try to finetune one of the pretrained models? If something is wrong with the data then the metrics should start to become worse.
Thanks for replying, I will try the pretrained model today.
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Related Issues (20)
- How to assign the correspoding vertices of smpl with respect to skeleton points? HOT 6
- How to train model with human3.6m datasets HOT 1
- Preprocess of ground truth keypoints_3d on Human3.6M HOT 1
- question when I read the code HOT 1
- questions about "mesh_downsampling.npz" HOT 1
- Regarding fully connected baseline HOT 4
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- How to get 3d joints from demo.py and visualize it HOT 3
- About the SMPLParamRegressor
- Praise from a newbie HOT 1
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- Running βdemo.py' can't get good results HOT 2
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- Loading Resnet50 pretrained?
- Asking for the weight of losses
- how to retrain this model in a new dataset with the real SMPL model
- preprocess datasets of h36m.py
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