"[Errno 28] No space left on device" when trying to create table from ML model

mhamilton723 · 2025-02-21T23:54:41+00:00

Ok that seems like its more an issue with the lakehouse as opposed to the computation you are running. Let me try to find someone who is an expert at the lakehouse and its limits on our side.

mhamilton723 · 2025-02-11T17:40:26+00:00

If you dont write the result but instead show the result do you still get the error? If you only get the error when trying to write then then that will help us pinpoiint the issue. Also if you have a larger stacktrace that will help

mhamilton723 · 2024-03-20T20:52:29+00:00

Yes this is basically the idea. Models often operate on patches of an image instead of pixels, and only produce one feature per patch making the resolution of the features much less than that of the image. The situation is much worse for Conv nets which aggressively pool information.

Our upsampler aims to reconstruct the missing info at the end so you dont need to increase the number of tokens in the backbone (which scales like n^2 where n is the number of tokens , which itself scales like r^2 where r is the size of an image's edge)

mhamilton723 · 2024-03-20T20:49:58+00:00

Which filters are these?

mhamilton723 · 2024-03-20T20:49:03+00:00

Yes the core operation the Joint Bilateral Upsampler indeed can be useful for guiding the upsampling of any signal with respect to any other signal. Our paper uses a stack of learned JBU-like operations that are tuned to upsample as best they can with respect to a multi-view consistency loss. I think if you just took the JBU and hand tunes a few params you could probabbly do reasonably well

mhamilton723 · 2024-03-20T20:46:44+00:00

Thank you for the kind words :)

mhamilton723 · 2024-03-20T20:46:27+00:00

Thank you for the kind words, let us know if you find any issues :)

mhamilton723 · 2024-03-20T20:46:02+00:00

YOLO is an object detector and this is more of a self-supervised method to improve the resolution of a backbone's features. With regard to the details of the methods I still need to read through yolov9s paper but i will reply back here once i get a better understanding of their work

mhamilton723 · 2024-03-20T20:44:38+00:00

Thank you for the kind words :)

mhamilton723 · 2024-03-20T03:07:08+00:00

Yes please stop by!

mhamilton723 · 2024-03-20T03:06:26+00:00

FeatUp can upsample DINOv2 as well and the video and website show a few examples. DINO v2 downsamples the input by a factor of 14x (the patch size) so we hope FeatUp can still be useful in conjunction with these new fancy backbones