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- 7 种模型架构可用于单类或者多类的分割任务 (包括Unet)
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- 7 种编码器网络
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- 所有的编码器都有预训练权重,可以更快更好地收敛
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- - 相比于python下的GPU前向推理速度具有2倍或以上的优势 , cpu下保持速度一致. (Unet测试于RTX 2070S).
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+ - 相比于python下的GPU前向推理速度具有30%或以上的提速 , cpu下保持速度一致. (Unet测试于RTX 2070S).
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### [ 📚 Libtorch教程 📚] ( https://github.com/AllentDan/LibtorchTutorials )
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Original file line number Diff line number Diff line change @@ -16,7 +16,7 @@ The main features of this library are:
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- 7 models architectures for binary and multi class segmentation (including legendary Unet)
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- 7 available encoders
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- All encoders have pre-trained weights for faster and better convergence
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- - 2x or more faster than pytorch cuda inferece , same speed for cpu. (Unet tested in rtx 2070s).
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+ - 35% or more inference speed boost compared with pytorch cuda, same speed for cpu. (Unet tested in rtx 2070s).
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### [ 📚 Libtorch Tutorials 📚] ( https://github.com/AllentDan/LibtorchTutorials/tree/master )
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