Table 2 Hyperparameters and Training Outcomes.

From: Dense monocular depth estimation for stereoscopic vision based on pyramid transformer and multi-scale feature fusion

 

Scheme 1

Scheme 2

Scheme 3

Scheme 4

Scheme 5

Scheme 6

Scheme 7

Scheme 8

variable name

 depth_datum"

0.7

0.7

0.7

0.7

0.7

0.7

0.7

0.7

 loss_seg_penality_factor"

3.0

3.0

3.0

3.0

3.0

3.0

3.0

3.0

 loss_ratio_out_factor"

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

 loss_mse_factor"

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

 loss_ssim_factor"

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

 loss_smooth_factor"

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

 loss_ratio_out_attenuation_factor"

6.0

6.0

6.0

6.0

6.0

6.0

6.0

6.0

 loss_segmentation_factor"

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

 loss_depth_in_factor"

0.5

0.6

0.5

0.6

0.5

0.6

0.5

0.6

 loss_depth_out_factor"

0.5

0.4

0.5

0.4

0.5

0.4

0.5

0.4

 loss_fine_threshold_factor"

0.4

0.4

0.5

0.5

0.5

0.4

0.5

0.5

 loss_coarse_threshod_factor"

5.0

5.0

5.0

7.0

5.0

5.0

5.0

5.0

 fx

2.5

2.5

2.5

2.5

3.5

3.5

2.5

2.5

 fx_min

0.5

0.3

0.5

0.5

0.5

0.3

0.5

0.5

Loss Type (epoch = 10)

 loss_seg_penality

0.0742

0.1314

0.1006

0.2111

0.1071

0.1021

0.1512

0.1500

 loss_smoothness

0.0038

0.0035

0.0032

0.0084

0.0031

0.0039

0.0032

0.0031

 loss_ssim

0.0262

0.0149

0.0120

0.0285

0.0151

0.0162

0.0148

0.0150

 loss_mse

0.0757

0.0440

0.0885

0.2643

0.1467

0.1300

0.0771

0.0791

 total loss

0.1800

0.1937

0.2043

0.5123

0.2720

0.2522

0.2463

0.2472

Loss Type (epoch = 45)

 

 loss_seg_penality

0.0002

0.0012

0.0007

0.0009

0.0005

0.0019

0.0002

0.0003

 loss_smoothness

0.0033

0.0034

0.0034

0.0037

0.0037

0.0032

0.0031

0.0032

 loss_ssim

0.0180

0.0203

0.0159

0.0213

0.0224

0.0215

0.0130

0.0126

 loss_mse

0.0504

0.0576

0.0578

0.0630

0.0651

0.0643

0.0361

0.0374

 total loss

0.0718

0.0825

0.0778

0.0889

0.0918

0.0909

0.0524

0.0535

 accuracy

0.980

0.975

0.983

0.941

0.984

0.979

0.996

0.995

  1. Significant values are in [bold].