Open
Description
Dear,
Thanks for your great work!
With your offered code and hyper-parameters, I get the results as follows:
2022-10-01 04:03:09,229 - mmseg - INFO - Iter(val) [16000] mIoU: 0.3869, mAcc: 0.5037, aAcc: 0.8005
2022-10-01 05:56:34,575 - mmseg - INFO - Iter(val) [32000] mIoU: 0.4353, mAcc: 0.5557, aAcc: 0.8148
2022-10-01 07:49:43,813 - mmseg - INFO - Iter(val) [48000] mIoU: 0.4535, mAcc: 0.5794, aAcc: 0.8188
2022-10-01 09:42:33,149 - mmseg - INFO - Iter(val) [64000] mIoU: 0.4523, mAcc: 0.5758, aAcc: 0.8216
2022-10-01 11:35:34,234 - mmseg - INFO - Iter(val) [80000] mIoU: 0.4655, mAcc: 0.5783, aAcc: 0.8256
2022-10-01 13:28:33,442 - mmseg - INFO - Iter(val) [96000] mIoU: 0.4648, mAcc: 0.5726, aAcc: 0.8279
2022-10-01 15:21:28,416 - mmseg - INFO - Iter(val) [112000] mIoU: 0.4678, mAcc: 0.5798, aAcc: 0.8252
2022-10-01 17:14:35,033 - mmseg - INFO - Iter(val) [128000] mIoU: 0.4683, mAcc: 0.5806, aAcc: 0.8270
2022-10-01 19:07:43,025 - mmseg - INFO - Iter(val) [144000] mIoU: 0.4729, mAcc: 0.5804, aAcc: 0.8279
2022-10-01 21:00:48,207 - mmseg - INFO - Iter(val) [160000] mIoU: 0.4758, mAcc: 0.5841, aAcc: 0.8293
It seems a few lower than yours.
Could you provide the model weights that reach 48.1% ?
Sincerely.
Metadata
Assignees
Labels
No labels