Comments (7)
Excellent, this is exactly what I need. Thank you for the quick and thorough responses!
from ict.
Let me be specific: for working with 256x256 greyscale imgs.
from ict.
Could you provide some statistics of your dataset? BTW, you could also scale the parameters of transformer (layers, embedding dimensions etc.) to decrease the computational cost.
from ict.
Hey, thanks for getting back to me!
My data are 256x256 patches sampled randomly from much larger (about 2000x3000) single-class one-channel images. At test time, I only need to do completions on the same type of patches, for the same 128x128 mask at the center on all test imgs.
from ict.
Hey, thanks for getting back to me!
My data are 256x256 patches sampled randomly from much larger (about 2000x3000) single-class one-channel images. At test time, I only need to do completions on the same type of patches, for the same 128x128 mask at the center on all test imgs.
Great~ How many such 2000x3000 images do you have?
from ict.
Around 8000, but I will have around 90,000 total soon.
from ict.
Around 8000, but I will have around 90,000 total soon.
Got it. I think the training (90,000 images) will require about 2 days using 8xV100 (32G). Of course you can also reduce the batch size to make it work well on single GPU, which will require longer training time.
from ict.
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from ict.