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gnerate all mask spend 20s, it is too long #25
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same to me on my mac. |
Thanks for your interest in our work. It seems that there is either bug in the code or you have not used it improperly. At this moment, we cannot help debug in a timely manner. If you provide more details, we will try to help as soon as possible. See the demo of running MobileSAM on CPU is available here for checking the speed: https://huggingface.co/spaces/dhkim2810/MobileSAM |
ok |
Yet another demo is available at https://mobilesam.glitch.me/ to run everything on your own browser. Enjoy it~~ |
sorry i had to take action and remove some stuff and put on pause because
they suspended system issues
El sáb., 1 de julio de 2023 10:17 a. m., Chaoning Zhang <
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问题先关闭吧,我在3090测试和还是原始的sam差不多,我再多了解一下原理就,多谢你们辛苦的付出
Vanessaaui ***@***.***> 于2023年7月2日周日 00:31写道:
… sorry i had to take action and remove some stuff and put on pause because
they suspended system issues
El sáb., 1 de julio de 2023 10:17 a. m., Chaoning Zhang <
***@***.***> escribió:
> Closed #25 <#25> as
> completed.
>
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Thanks for your interest in our work. Note that MobileSAM makes the image encoder lightweight without changing the decoder (like 8ms on the encoder and 4ms on the decoder). Since we mainly target the anything mode (1 times image encoder and 1 times decoder) instead of everything mode (1 times image encoder and 32x32 times decoder), see the paper for definition difference (Anything mode is the foundation task while everything mode is just a downstream task as indicated in the original SAM paper). "gnerate all mask" seems to suggest that you are using everything mode. For everything mode, even though our encoder is much faster than that of the original SAM(roughly 8ms vs 450ms), it cannot save too much time for the whole pipeline since most of the time is spent on the 32x32 times decoder. One way to mitigate this is to use smaller number of grids (like 10x10 or 5x5) to make the decoder consume less time, since many redundant masks are generated in the case of 32x32 grids. I hope this addresses your issues, otherwise, please kindly let us know. We are also currently trying to make the image decoder more lightweight by distilling it with smaller one as we did for image encoder. Stayed tuned for our progress. If you have more issues, please kindly let us know and we might not be able to respond in a timely manner, but will try our best.
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