29 lines
1.3 KiB
Markdown
29 lines
1.3 KiB
Markdown
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# 4. Training Tricks
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MMSegmentation support following training tricks out of box.
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## Different Learning Rate(LR) for Backbone and Heads
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In semantic segmentation, some methods make the LR of heads larger than backbone to achieve better performance or faster convergence.
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In MMSegmentation, you may add following lines to config to make the LR of heads 10 times of backbone.
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```python
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optimizer_config=dict(
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paramwise_cfg = dict(
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custom_keys={
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'head': dict(lr_mult=10.)}))
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```
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With this modification, the LR of any parameter group with `'head'` in name will be multiplied by 10.
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You may refer to [MMCV doc](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.DefaultOptimizerConstructor) for further details.
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## Online Hard Example Mining (OHEM)
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We implement pixel sampler [here](https://github.com/open-mmlab/mmsegmentation/tree/master/mmseg/core/seg/sampler) for training sampling.
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Here is an example config of training PSPNet with OHEM enabled.
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```python
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_base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py'
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model=dict(
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decode_head=dict(
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sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=100000)) )
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```
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In this way, only pixels with confidence score under 0.7 are used to train. And we keep at least 100000 pixels during training.
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