Depth performance using ResNet-50 (Single-task performance)

[Simon4Yan]

Thank you very much for sharing the wonderful code!

I meet a question when running the code: while I can get a similar accuracy on Segmentation (43.5 on mIoU) using ResNet-50, the accuracy on depth is not so good (0.614 RMSE). I have read related issues (#1) and (#5). But I still cannot address the question in my case, could you please give me some suggestions about the single-task experiment in Depth?

Thanks and Regards

Epoch 100/100
----------
Adjusted learning rate to 0.00000
Train ...
Epoch: [99][ 0/99]      Loss depth 1.0003e-01 (1.0003e-01)      Loss Total 1.0003e-01 (1.0003e-01)
Epoch: [99][25/99]      Loss depth 1.4344e-01 (1.2583e-01)      Loss Total 1.4344e-01 (1.2583e-01)
Epoch: [99][50/99]      Loss depth 1.3219e-01 (1.2832e-01)      Loss Total 1.3219e-01 (1.2832e-01)
Epoch: [99][75/99]      Loss depth 1.2006e-01 (1.3160e-01)      Loss Total 1.2006e-01 (1.3160e-01)
Results for depth prediction
rmse           0.2232
log_rmse       0.0887
Evaluate ...
Save model predictions to ./results/NYUD/resnet50/single_task/depth/results
Files already downloaded
Initializing dataloader for NYUD val set
Number of dataset images: 654
Evaluate the saved images (depth)
Evaluating depth: 0 of 654 objects
Evaluating depth: 500 of 654 objects
Results for Depth Estimation
rmse           0.6204
log_rmse       0.2119
No new best depth estimation model 0.614 -> 0.620
Checkpoint ...
Evaluating best model at the end
Save model predictions to ./results/NYUD/resnet50/single_task/depth/results
Files already downloaded
Initializing dataloader for NYUD val set
Number of dataset images: 654
Evaluate the saved images (depth)
Evaluating depth: 0 of 654 objects
Evaluating depth: 500 of 654 objects
Results for Depth Estimation
rmse           0.6204
log_rmse       0.2119