Comments (3)
The performance I reproduced 63.0 is lower than AA&LR ACM MM’2021 (63.9 VOC2012 valid announced in paper).
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For experiments with reg loss
, the log and weight files can be found at #3 (comment). The full test code is now available here. I did use multiscale and flip inference before CRF. They're common post-processing steps for semantic segmentation, so I didn't expatriate the details in paper.
Since I didn't save the weights without reg loss
, I repeated the experiments and got close results again. The new log and weights of the reproduced results are available here (log & weights).
I don't what's wrong with your experiments, but it looks like the reproduction is fine.
from afa.
Thank you for your patient reply. I'll try it soon.
from afa.
Related Issues (20)
- where are test codes HOT 2
- CNN & Trans CAM
- About PAR
- May I ask which part of the code of CAM to generate pseudo mask?
- 为什么计算seg loss的时候使用refined_aff_label? HOT 1
- Fairness concern.
- Using pre-trained weights from SegFormer confused me HOT 2
- the log of coco dataset HOT 1
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [256]] is at version 3; expected version 2 instead HOT 3
- COCO dataset weights and segmentation results
- Hello, can you please share the model weights on VOC dataset?
- Welcome update to OpenMMLab 2.0
- labels_camp = torch.from_numpy(labels_camp).permute([0,3,1,2]) HOT 1
- How to visualize attention?
- PyCharm to a server for training
- Duplicate GPU detected
- No module named 'bilateralfilter' HOT 1
- python 3.7 cuda 11.1 torch 1.8 got error HOT 1
- single gpu train HOT 4
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