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3. Loss Functions

GitHub Link : https://github.com/satyajitghana/ProjektDepth/blob/master/notebooks/11_DepthModel_ModelTrain_PlayWithLossFunctions.ipynb Colab Link : https://colab.research.google.com/github/satyajitghana/ProjektDepth/blob/master/notebooks/11_DepthModel_ModelTrain_PlayWithLossFunctions.ipynb

Segmentation Loss Functions:

  • BCEWithLogits
  • DiceLoss
  • BCEDice
  • TverskyLoss
  • BCETversky

Depth Loss Functions:

  • BerHu
  • GradLoss
  • BCEWithLogits
  • RMSE
  • SSIM

Various combinations of the above losses were tried with 1/16th of the dataset

A Small experiment was done to check if 30M params is really required or no, we found that 15M params model performed similar to 30M, so the higher param model was scraped

With 30M Params:

image

With 15M Params:

image

Playing with Loss Functions

Loss Functions Comparison
Seg Loss Funcion Depth Loss Function mIOU mRMSE
BCE BCE 0.1278 0.1149
Dice BCE 0.4515 0.0858
Tversky BCE 0.3221 0.1018
BCEDice BCE 0.4578 0.0993
BCETversky BCE 0.3831 0.0936
BCEDice BerHu 0.4214 0.2882
BCEDice RMSE 0.4366 0.0774
BCEDice Grad 0.4542 0.1795
BCEDice SSIM 0.4413 0.1304

Note

- mIOU: higher is better - mRMSE: lower is better

1. seg_loss = BCEWithLogits, depth_loss = BCEWithLogits

bce_bce

bce_bce 
mIOU : 0.12789911031723022
mRMSE : 0.11494194716215134
total time : 196.1347 s

2. seg_loss = DiceLoss, depth_loss = BCEWithLogits

dice_bce

dice_bce 
mIOU : 0.4515075087547302
mRMSE : 0.08582810312509537
total time : 193.0364 s

3. seg_loss = TverskyLoss, depth_loss = BCEWithLogits

tversky_bce

tversky_bce 
mIOU : 0.32213953137397766
mRMSE : 0.10182604193687439
total time : 193.5620 s

4. seg_loss = BCEDiceLoss, depth_loss = BCEWithLogits

bcedice_bce

bcedice_bce 
mIOU : 0.4578476846218109
mRMSE : 0.09939917922019958
total time : 191.8000 s

5. seg_loss = BCETverskyLoss, depth_loss = BCEWithLogits

bcetversky_bce

bcetversky_bce 
mIOU : 0.3831656873226166
mRMSE : 0.0936645045876503
total time : 192.6121 s

6. seg_loss = BCEDiceLoss, depth_loss = BCEWithLogits

bcedice_bce

bcedice_bce 
mIOU : 0.4485453963279724
mRMSE : 0.12491746991872787
total time : 193.3488 s

7. seg_loss = BCEDiceLoss, depth_loss = BerHuLoss

bcedice_berhu

bcedice_berhu 
mIOU : 0.42147812247276306
mRMSE : 0.2882708013057709
total time : 193.7522 s

8. seg_loss = BCEDiceLoss, depth_loss = RMSELoss

bcedice_rmse

bcedice_rmse 
mIOU : 0.4366089999675751
mRMSE : 0.07745874673128128
total time : 180.7616 s

9. seg_loss = BCEDiceLoss, depth_loss = GradLoss

bcedice_grad

bcedice_grad 
mIOU : 0.4542521834373474
mRMSE : 0.1795133352279663
total time : 185.2947 s

10. seg_loss = BCEDiceLoss, depth_loss = SSIMLoss

bcedice_ssim

bcedice_ssim 
mIOU : 0.4413087069988251
mRMSE : 0.1304335743188858
total time : 189.7473 s