"segment-anything" is slow about efficientsam

[klightz]

Hi, thanks for your interest, the segment-anything notebook example is work-in-progress and currently it only supports CPU version (you can find that tensors are forced to cpu in the function) with a naive & straight forward algorithm. We will adapt a quicker version in the future.

[sipie800]

same here, more than 30s on rtx3090, not efficient at all

[yformer]

@GitChanyoung, thanks for your interest! We will take a look for segment everything.

[silinsi]

I experienced similar thing, it seems that code provided in [EfficientSAM_segment_everything_example.ipynb] is not adapted to gpu , i tried to move the network to gpu but this does not help a lot( probably because my modification not complete ), saving around ten seconds but it still costs around five to ten seconds.
Moreover, to the same image, it runs a little faster after first run.

[sulaimanvesal]

When running on a CPU the Tiny EfficienetSAM, the performance of this model is significantly slower than that of FastSAM-S. After conducting a thorough comparison between FastSAM and EfficientSAM.

The following result is running the code on Google Collab:

Inference using:  efficientsam_ti_cpu.jit
Input size: torch.Size([3, 512, 1024])
Preprocess Time: 79.8783 ms
Inference Time: 6939.1549 ms

[liutongkun]

same here, more than 30s on rtx3090, not efficient at all

on my 3090ti，i7 12700， it's 30-40ms per image. The problem is that the speed will be slower when running the first inference, I'm not sure why