Unfair experimental settings. (DeepLab v3+ vs. DeepLab v2)

[zhijiew]

In your experiments, your method used DeepLab v3+ as the backbone to compare with other methods that use DeepLab v2, which is totally unfair. Can you report the results based on DeepLab v2?

[sharat29ag]

Yes please update the results.

[sharat29ag]

For the warmup you have used AdaptNet which uses DeeplabV2 as the Segmentation model, was that also changed to DeeplabV3+? If yes can you please report the initial result after warmup?

[munanning]

Sorry for the delay. @zhijiew @sharat29ag
The result on DeeplabV2 is implemented. Beyond expectation, the final result is 65.16 (mIoU), better than 64.89 (mIoU) with DeeplabV3+.
Here is the origin training log:
2020-11-01 12:14:55,783 INFO Iter 15000 Loss: 0.1928
2020-11-01 12:14:55,784 INFO Iter 15000 Source Loss: 0.0000
2020-11-01 12:14:55,785 INFO Overall Acc: : 0.9418029923712912
2020-11-01 12:14:55,785 INFO Mean Acc : : 0.7246892190272104
2020-11-01 12:14:55,785 INFO FreqW Acc : : 0.893493677898871
2020-11-01 12:14:55,786 INFO Mean IoU : : 0.6516297024999936
2020-11-01 12:14:55,786 INFO 0: 0.9693999962471279
2020-11-01 12:14:55,786 INFO 1: 0.7685028486723834
2020-11-01 12:14:55,787 INFO 2: 0.8895210625419824
2020-11-01 12:14:55,787 INFO 3: 0.33887047797652403
2020-11-01 12:14:55,787 INFO 4: 0.47570431561588006
2020-11-01 12:14:55,787 INFO 5: 0.5278077449295415
2020-11-01 12:14:55,788 INFO 6: 0.5979519384158949
2020-11-01 12:14:55,788 INFO 7: 0.6525470266354098
2020-11-01 12:14:55,788 INFO 8: 0.9034188835393306
2020-11-01 12:14:55,788 INFO 9: 0.506204355591972
2020-11-01 12:14:55,789 INFO 10: 0.9236311508393851
2020-11-01 12:14:55,789 INFO 11: 0.7771101286799015
2020-11-01 12:14:55,789 INFO 12: 0.5364837525760412
2020-11-01 12:14:55,789 INFO 13: 0.9200637629029278
2020-11-01 12:14:55,790 INFO 14: 0.5212897908120947
2020-11-01 12:14:55,790 INFO 15: 0.5616047800928549
2020-11-01 12:14:55,790 INFO 16: 0.27918147195439025
2020-11-01 12:14:55,790 INFO 17: 0.5421158030330913
2020-11-01 12:14:55,790 INFO 18: 0.6895550564431461
2020-11-01 12:14:57,555 INFO Best iou until now is 0.6516297024999936

Our training settings is based on the CAG_UDA (Nips 2019, https://github.com/RogerZhangzz/CAG_UDA). However, the author has written the DeeplabV3+ to DeeplabV2. The only thing I can do is to report my result and framework honestly.
And here is the origin codes and files for DeeplabV2: https://drive.google.com/drive/folders/1DnwzCabQnUbDeg6xOvkAeJZFG9V95g6n?usp=sharing

[munanning]

For the warmup you have used AdaptNet which uses DeeplabV2 as the Segmentation model, was that also changed to DeeplabV3+? If yes can you please report the initial result after warmup?

The reported warmup model is a DeeplabV3+ model. For a fair comparison, I reused the warmup model from the CAG_UDA (Nips 2019, https://github.com/RogerZhangzz/CAG_UDA), just here

The origin report is here:
INFO:ptsemseg:Mean IoU : : 0.4253894928867592
INFO:ptsemseg:0: 0.882774709553075
INFO:ptsemseg:1: 0.44709892359884384
INFO:ptsemseg:2: 0.8203015163263928
INFO:ptsemseg:3: 0.314210933498157
INFO:ptsemseg:4: 0.24375954741277261
INFO:ptsemseg:5: 0.37366047858246787
INFO:ptsemseg:6: 0.3780563158565676
INFO:ptsemseg:7: 0.2466602204997033
INFO:ptsemseg:8: 0.8171751060949118
INFO:ptsemseg:9: 0.29433954349025376
INFO:ptsemseg:10: 0.7082770339997182
INFO:ptsemseg:11: 0.5259153279163223
INFO:ptsemseg:12: 0.2275386269806841
INFO:ptsemseg:13: 0.8395305067066231
INFO:ptsemseg:14: 0.1847198116100887
INFO:ptsemseg:15: 0.28772643642333734
INFO:ptsemseg:16: 0.1779052576284909
INFO:ptsemseg:17: 0.1595831186206701
INFO:ptsemseg:18: 0.15316695004934425

I hope my reply can dispel your doubts @sharat29ag .

[sharat29ag]

For the warmup you have used AdaptNet which uses DeeplabV2 as the Segmentation model, was that also changed to DeeplabV3+? If yes can you please report the initial result after warmup?

The reported warmup model is a DeeplabV3+ model. For a fair comparison, I reused the warmup model from the CAG_UDA (Nips 2019, https://github.com/RogerZhangzz/CAG_UDA), just here

The origin report is here: INFO:ptsemseg:Mean IoU : : 0.4253894928867592 INFO:ptsemseg:0: 0.882774709553075 INFO:ptsemseg:1: 0.44709892359884384 INFO:ptsemseg:2: 0.8203015163263928 INFO:ptsemseg:3: 0.314210933498157 INFO:ptsemseg:4: 0.24375954741277261 INFO:ptsemseg:5: 0.37366047858246787 INFO:ptsemseg:6: 0.3780563158565676 INFO:ptsemseg:7: 0.2466602204997033 INFO:ptsemseg:8: 0.8171751060949118 INFO:ptsemseg:9: 0.29433954349025376 INFO:ptsemseg:10: 0.7082770339997182 INFO:ptsemseg:11: 0.5259153279163223 INFO:ptsemseg:12: 0.2275386269806841 INFO:ptsemseg:13: 0.8395305067066231 INFO:ptsemseg:14: 0.1847198116100887 INFO:ptsemseg:15: 0.28772643642333734 INFO:ptsemseg:16: 0.1779052576284909 INFO:ptsemseg:17: 0.1595831186206701 INFO:ptsemseg:18: 0.15316695004934425

I hope my reply can dispel your doubts @sharat29ag .

Thank you for the response will look to it.

[luyvlei]

Sorry for the delay. @zhijiew @sharat29ag The result on DeeplabV2 is implemented. Beyond expectation, the final result is 65.16 (mIoU), better than 64.89 (mIoU) with DeeplabV3+. Here is the origin training log: 2020-11-01 12:14:55,783 INFO Iter 15000 Loss: 0.1928 2020-11-01 12:14:55,784 INFO Iter 15000 Source Loss: 0.0000 2020-11-01 12:14:55,785 INFO Overall Acc: : 0.9418029923712912 2020-11-01 12:14:55,785 INFO Mean Acc : : 0.7246892190272104 2020-11-01 12:14:55,785 INFO FreqW Acc : : 0.893493677898871 2020-11-01 12:14:55,786 INFO Mean IoU : : 0.6516297024999936 2020-11-01 12:14:55,786 INFO 0: 0.9693999962471279 2020-11-01 12:14:55,786 INFO 1: 0.7685028486723834 2020-11-01 12:14:55,787 INFO 2: 0.8895210625419824 2020-11-01 12:14:55,787 INFO 3: 0.33887047797652403 2020-11-01 12:14:55,787 INFO 4: 0.47570431561588006 2020-11-01 12:14:55,787 INFO 5: 0.5278077449295415 2020-11-01 12:14:55,788 INFO 6: 0.5979519384158949 2020-11-01 12:14:55,788 INFO 7: 0.6525470266354098 2020-11-01 12:14:55,788 INFO 8: 0.9034188835393306 2020-11-01 12:14:55,788 INFO 9: 0.506204355591972 2020-11-01 12:14:55,789 INFO 10: 0.9236311508393851 2020-11-01 12:14:55,789 INFO 11: 0.7771101286799015 2020-11-01 12:14:55,789 INFO 12: 0.5364837525760412 2020-11-01 12:14:55,789 INFO 13: 0.9200637629029278 2020-11-01 12:14:55,790 INFO 14: 0.5212897908120947 2020-11-01 12:14:55,790 INFO 15: 0.5616047800928549 2020-11-01 12:14:55,790 INFO 16: 0.27918147195439025 2020-11-01 12:14:55,790 INFO 17: 0.5421158030330913 2020-11-01 12:14:55,790 INFO 18: 0.6895550564431461 2020-11-01 12:14:57,555 INFO Best iou until now is 0.6516297024999936

Our training settings is based on the CAG_UDA (Nips 2019, https://github.com/RogerZhangzz/CAG_UDA). However, the author has written the DeeplabV3+ to DeeplabV2. The only thing I can do is to report my result and framework honestly. And here is the origin codes and files for DeeplabV2: https://drive.google.com/drive/folders/1DnwzCabQnUbDeg6xOvkAeJZFG9V95g6n?usp=sharing

Is there any mistake, I use the model of DeepLabV3+ from https://github.com/RogerZhangzz/CAG_UDA to train cityscape, and got the following result:
2021-11-19 08:53:38,453 INFO Mean IoU : : 0.7482995820400281
2021-11-19 08:53:38,453 INFO Mean IoU (16) : : 0.752068355974944
2021-11-19 08:53:38,453 INFO Mean IoU (13) : : 0.7987122101405965
2021-11-19 08:53:38,454 INFO 0: 0.9765658041915719
2021-11-19 08:53:38,454 INFO 1: 0.8224862674039519
2021-11-19 08:53:38,455 INFO 2: 0.9112979273421086
2021-11-19 08:53:38,455 INFO 3: 0.5291329863290484
2021-11-19 08:53:38,455 INFO 4: 0.5701830669298439
2021-11-19 08:53:38,456 INFO 5: 0.5505189105124582
2021-11-19 08:53:38,456 INFO 6: 0.6062819018186969
2021-11-19 08:53:38,456 INFO 7: 0.7005523425411562
2021-11-19 08:53:38,456 INFO 8: 0.9159354501634949
2021-11-19 08:53:38,457 INFO 9: 0.6386477854657843
2021-11-19 08:53:38,457 INFO 10: 0.940211061631527
2021-11-19 08:53:38,457 INFO 11: 0.778340032763567
2021-11-19 08:53:38,458 INFO 12: 0.5697296981527584
2021-11-19 08:53:38,458 INFO 13: 0.9381809896493891
2021-11-19 08:53:38,458 INFO 14: 0.7726736052796462
2021-11-19 08:53:38,459 INFO 15: 0.8678234591230659
2021-11-19 08:53:38,459 INFO 16: 0.7732769724159989
2021-11-19 08:53:38,459 INFO 17: 0.6281585092931333
2021-11-19 08:53:38,460 INFO 18: 0.7276952877533335
2021-11-19 08:53:40,287 INFO Best iou until now is 0.7482995820400281
I train the model(Using ImageNet pretrained backbone) with the following Hyperparameter:

train_iters: 90000

optimizer:
    name: 'SGD'
    lr: 0.001
    weight_decay: 5.0e-4
    momentum: 0.9

lr_schedule:
    name: 'poly_lr'
    T_max: 90000

dataset:
    name: cityscapes
    rootpath: dataset/CityScape
    split: train
    img_rows: 1024
    img_cols: 2048
    batch_size: 5
    img_norm: True
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]
    n_class: 19

 augmentations:
     gamma: 0.2
     brightness: 0.5
     saturation: 0.5
     contrast: 0.5
     rcrop: [1024, 512]
     hflip: 0.5

I using amp to save memory so that I can train the model with single 2080ti.

[sharat29ag]

Sorry for the delay. @zhijiew @sharat29ag The result on DeeplabV2 is implemented. Beyond expectation, the final result is 65.16 (mIoU), better than 64.89 (mIoU) with DeeplabV3+. Here is the origin training log: 2020-11-01 12:14:55,783 INFO Iter 15000 Loss: 0.1928 2020-11-01 12:14:55,784 INFO Iter 15000 Source Loss: 0.0000 2020-11-01 12:14:55,785 INFO Overall Acc: : 0.9418029923712912 2020-11-01 12:14:55,785 INFO Mean Acc : : 0.7246892190272104 2020-11-01 12:14:55,785 INFO FreqW Acc : : 0.893493677898871 2020-11-01 12:14:55,786 INFO Mean IoU : : 0.6516297024999936 2020-11-01 12:14:55,786 INFO 0: 0.9693999962471279 2020-11-01 12:14:55,786 INFO 1: 0.7685028486723834 2020-11-01 12:14:55,787 INFO 2: 0.8895210625419824 2020-11-01 12:14:55,787 INFO 3: 0.33887047797652403 2020-11-01 12:14:55,787 INFO 4: 0.47570431561588006 2020-11-01 12:14:55,787 INFO 5: 0.5278077449295415 2020-11-01 12:14:55,788 INFO 6: 0.5979519384158949 2020-11-01 12:14:55,788 INFO 7: 0.6525470266354098 2020-11-01 12:14:55,788 INFO 8: 0.9034188835393306 2020-11-01 12:14:55,788 INFO 9: 0.506204355591972 2020-11-01 12:14:55,789 INFO 10: 0.9236311508393851 2020-11-01 12:14:55,789 INFO 11: 0.7771101286799015 2020-11-01 12:14:55,789 INFO 12: 0.5364837525760412 2020-11-01 12:14:55,789 INFO 13: 0.9200637629029278 2020-11-01 12:14:55,790 INFO 14: 0.5212897908120947 2020-11-01 12:14:55,790 INFO 15: 0.5616047800928549 2020-11-01 12:14:55,790 INFO 16: 0.27918147195439025 2020-11-01 12:14:55,790 INFO 17: 0.5421158030330913 2020-11-01 12:14:55,790 INFO 18: 0.6895550564431461 2020-11-01 12:14:57,555 INFO Best iou until now is 0.6516297024999936

Our training settings is based on the CAG_UDA (Nips 2019, https://github.com/RogerZhangzz/CAG_UDA). However, the author has written the DeeplabV3+ to DeeplabV2. The only thing I can do is to report my result and framework honestly. And here is the origin codes and files for DeeplabV2: https://drive.google.com/drive/folders/1DnwzCabQnUbDeg6xOvkAeJZFG9V95g6n?usp=sharing

Hi, @munanning please share the initial weights for V2. Also the selection list contains 297 images, please share list of 150 images for fair comparison.

[munanning]

@sharat29ag
The answer is 'the same'.
The weight can be found in Adaptset (https://github.com/wasidennis/AdaptSegNet).
The list is the same as the V3 version.

[sharat29ag]

Thanks.

[sharat29ag]

@munanning does the warmup weights at sgate1 for GTA->City and Synthia->City were same?