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alexklwong / calibrated-backprojection-network

PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
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Different results between validation while training, and testing #16

Closed rakshith95 closed 2 years ago

rakshith95 commented 2 years ago

Hello, I captured some data, and set it up in the VOID format, and train the model on that. While training, I get the best results as: 

    Step       MAE      RMSE      iMAE     iRMSE
   21000   119.343   200.820    56.941   120.669

But if I run the evaluation script with this best result model weights (21000) , I get very different results with much higher errors:

Evaluation results:
     MAE      RMSE      iMAE     iRMSE
3989.622  3996.284   517.797   540.038
     +/-       +/-       +/-       +/-
  55.942    58.938    51.691    54.052

This doesn't make sense to me since I'm using the same set of data for the validation and testing,similar to what is provided in the scripts. Do you have any ideas on why this could be happening?

rakshith95 commented 2 years ago

Ok looks like the min_predict_depth parameter was different between my train, and run scripts. Seems to have fixed it