Comments (1)
Yes, it depends on benchmark. In all dataset, ADE20K dataset has the largest variance. If you want to get stable result, trying to use cityscapes.
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Related Issues (20)
- The paddlepaddle version of SegNeXt
- Welcome update to OpenMMLab 2.0
- Inconsistencies in ADE20K model inference output for the same input image. HOT 3
- How to start training? HOT 1
- AssertionError: train_cfg specified in both outer field and model field
- Comparison with SegFormer-B5 HOT 1
- ImportError: cannot import name 'revert_sync_batchnorm' from 'mmcv.cnn.utils' HOT 1
- Training gpu consumption vs inference gpu consumption
- about AttentionModule
- If I want to train with 4 GPUs, do I need to change the code
- Evaluation Result on Cityscapes
- How to stop shuffle for test.py
- Config file for the i-SAID dataset
- Can you provide the IOU per class of IsAID dataset?
- 大佬们你们下载的哪个版本的mmcv 为什么我版本较老(mmcv-full1.6.0) 会报错 版本太新(mmcv r4)没有mmcv.runner? HOT 2
- mIoU and MPA
- 随机性问题
- reproduce official results
- 对于SegNext模型,我发现每次的测试结果都不同,这是为什么?
- 对于SegNext模型,我发现每次的测试结果都不同,这是为什么?
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