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Inconsistency between paper and code
See original GitHub issueHi, congratulations on your great work and acceptance in CVPR '21. Thanks for releasing the code and model weights.
In the paper, you mention using DeepLabv2 with ResNet101 backbone. However, your code actually makes use of a modified ASPP module (ClassifierModule2 in models/deeplabv2.py) while actually ClassifierModule has to be used for DeepLabv2. Similar issues were raised here and here which mention that this type of ASPP module is used in DeepLabv3+ which has a much better performance compared to DeepLabv2 (both issues were raised in Jan. 2020). Could you please confirm this point and if you have also performed experiments with the original DeepLabv2 model, could you report those results for a fair comparison with prior arts?
Issue Analytics
- State:
- Created 2 years ago
- Reactions:1
- Comments:6 (3 by maintainers)
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I think it’s better for you to report results with Deeplabv2(should not be difficult). The mainstream choice of segmentation is Deeplabv2, like SDCA, FADA paper.
And we can see the ablation study in our paper. The conventional self-training is trained by our modified ASPP, the performance is similar to the original ASPP in deeplabv2(45.9 mIoU reported in CRST).