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View Code? Open in Web Editor NEWPytorch implementation of paper Ladder Fine-tuning approach for SAM integrating complementary network.
License: MIT License
Pytorch implementation of paper Ladder Fine-tuning approach for SAM integrating complementary network.
License: MIT License
Thank you very much for sharing the code. We used the code you shared to train the model, but the prediction results we got were very poor, just like randomly generated results. Please tell me what causes this.
Hi, I would like to run a test using your parameters, can you provide the trained checkpoint?
Thanks for the great work!
I will be replicating your approach to fine tune SAM on custom few shot examples. The problem is I have only 8GB GPU memory available, and in the paper it is mentioned that two RTX 3090 graphics cards are used.
Would it be possible to replicate your approach having only 8GB memory space. If not, is there any way I can do fine tuning requiring less than 8GB memory e.g reducing batch_size etc
Your suggestions will be appreciated.
Regards,
Muhammad
Reference: https://github.com/ChaoningZhang/MobileSAM
Our project performs on par with the original SAM and keeps exactly the same pipeline as the original SAM except for a change on the image encode, therefore, it is easy to Integrate into any project.
MobileSAM is around 60 times smaller and around 50 times faster than original SAM, and it is around 7 times smaller and around 5 times faster than the concurrent FastSAM. The comparison of the whole pipeline is summarzed as follows:
Best Wishes,
Qiao
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