Comments (3)
Changing it to normal BN will decrease the performance of segmentation models.
Could you post the command you for us to repdocue?
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sorry for late reply. I run with :
- for deeplab:
PYTHONPATH='.':/home/xxx/anaconda3/envs/knet/bin/python3 MMCV_HOME=/home/xxx mim train mmseg /home/xxx/git/K-Net/configs/seg/knet/knet_s3_deeplabv3_r50-d8_80k_adamw_ade20k.py --gpus 1 --work-dir /home/xxx/git/K-Net/results --resume-from /home/xxx/git/K-Net/results/latest.pth
- for swin-t:
PYTHONPATH='.':/home/xxx/anaconda3/envs/smore/bin/python3 MMCV_HOME=/home/xxx mim train mmseg /home/xxx/git/K-Net/configs/seg/knet/knet_s3_upernet_swin-t_80k_adamw_ade20k.py --gpus 1 --work-dir /home/xxx/git/K-Net/swin_results
@ZwwWayne
from k-net.
It seems you only use 1 GPU. You should decrease the learning rate accordingly.
from k-net.
Related Issues (20)
- The link of trained model on coco instance segmentation is unaccessible, can you fix it? HOT 1
- KeyError: 'KNet is not in the models registry' when runing 'train.py' HOT 2
- About your semantic segmentation? HOT 1
- ModuleNotFoundError: No module named 'knet' HOT 3
- Implementation about kernel activation HOT 1
- 如何訓練自己的數據集
- About experiments setting HOT 1
- Training on custom dataset HOT 1
- Pre-trained Model
- how to calculate FPS HOT 1
- 'MaskPseudoSampler is already registered in bbox_sampler' HOT 5
- Logs for ADE20K
- the segm mAp result is zero
- OOM error when training on Cityscapes HOT 1
- 使用knet_s3_r50_fpn_1x_coco.py训练
- Can you provide the pre-trained models?
- An error occurred during training
- Welcome update to OpenMMLab 2.0 HOT 1
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- work-dir
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