Comments (1)
To freeze the iABN layers, you can set the require_grad=False in the config file. And then add the extra 10 epochs in the mutli-step parameters and the total epoch.
However, we don't use this lr schedule for training our network with this repository. Instead, we follow the following additional training protocol:
We start another round of training with the same initial lr, but halve the multi-step iteration steps as well as the total epoch.
(the model of this round is initialized from the previous one)
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Related Issues (20)
- kitti subset HOT 2
- not find efficientPS_singlegpu_sample HOT 1
- run on KITTI dataset HOT 1
- Fail to open efficientPS_cityscapes.zip HOT 3
- pre-trained model (cityscape) classes missing? HOT 1
- Issue Inference of EfficientPS not working
- which dataset to download from Cityscapes download directory
- Installation of EfficientPS not working due to dependency inplace_abn (inplace_abn not updated for CUDA Driver Version 11) HOT 1
- Mask dislocation when test
- Convert the model to onnx HOT 3
- Could not download the pretrained models using the links in README HOT 1
- I get a bad kitti segmentation result with kitti pre-trained model. HOT 1
- How to compute mIoU based on results
- A bug about the cityscapes_demo.py
- problem with converting cityscapes HOT 1
- NameError: name ' samples_ per_ gpu' is not defined HOT 2
- Pre-Trained Models is not available
- RuntimeError: CUDA error: invalid device function,The cuda in the environment matches the cuda in mmdetection. Why the error HOT 1
- ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part. HOT 1
- Semantic segmentation metrics are used for evaluation
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