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huixiancheng / CENet

[ICME 2022] CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
MIT License
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train paramter #10

Closed fengluodb closed 2 years ago

fengluodb commented 2 years ago

https://github.com/huixiancheng/CENet/blob/1ca48f1e9a6547d4361b921b491930b53ed681dd/config/arch/senet-2048p.yml#L67-L78 I have a question about these paramter. Your paramter is different from darknet which used. Will these paramter increase the model accuracy after you change it.

huixiancheng commented 2 years ago

Refer to issue #6. I didn't test its impact on performance. Perhaps it may slightly affect the performance of some classes.

fengluodb commented 2 years ago

Thank you for your reply. I have another quesetion about model train. Can you tell me how big do you usually set batchsize, H, W when you do experiment.

huixiancheng commented 2 years ago

Limited by resources, I usually start experiments with a resolution of 64*512. Training is faster with this setting. For batchsize, just make full use of the GPU under amp as much as possible.

fengluodb commented 2 years ago

When I use amp, loss will be nan after 4~5 epoch. Do you meet the condition?

huixiancheng commented 2 years ago

No, I have not met it on this codebase. However, I have met this problem on other codebases, see this issue. In that example, nan was caused by a numerical overflow.

fengluodb commented 2 years ago

Thank you. Your project and reply help me a lot.