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The input size generally depends on the compared methods. For example, the common size is 256 or 384 for the polyp segmentation task. You can also try using a larger size such as 1024. Note that higher resolutions introduce greater computational overhead and do not guarantee an increase in accuracy.
In fact, the standard U-Net Decoder is not efficient at processing high resolution images. You can try replacing the SAM2-UNet decoder with some classic efficient design, such as BiSeNet.
前辈您好,非常感谢您对这个优秀工作做出的贡献!
请问关于读入图像size = 352是怎么确定的?我如果想调大image_size的话,SAM2-UNet模型构建的地方需要做更改吗?
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