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License: Other
Lightweight and Progressively-Scalable Networks for Semantic Segmentation
License: Other
这篇论文的结果是在1080ti +TensorRT上对比的,也没说TensorRT采用的什么精度,而DDRNet等是在pytorch上跑的,不能直接比较
I trained the lpsnet-m on ImageNet 1k for 100 epoch with batch size 1024.The detials are as follows:
Optimizer:
Data augments:
Eval dataset:
result:
Then I use the pre-trained model to train a lpsnet-m on cityscapes for 180k iterations with batch size 16. The detials are as follows:
Loss function:
lr_scheduler:
Optimizer:
Data augments:
Eval dataset:
Result:
it didn't upsample to original input size, is this a bug ?
I'm wondering that did the miou score reported by table 5 in the paper use the ImageNet pre-trained?
And can you provide the exact training settings of ImageNet pre-train and cityscapes, including but not limited to data augments、optimizer settings and so on.
Thank you in advance!
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