The Speed of VITs and CNNs
8 comments
·May 2, 2025John7878781
djoldman
Interesting. Can you run your images through a segment model first and then only classify interesting boxes?
hedgehog
LeCun's technical assessments have borne out over a lot of years. The likely next step in scaling vision transformers is to treat the image as a MIP pyramid and use the transformer to adaptively sample out of that. Requires RL to train (tricky) but it would decouple compute footprint from input size.
tbalsam
As someone who has worked in computer vision ML for nearly a decade, this sounds like a terrible idea.
You don't need RL remotely for this usecase. Image resolution pyramids are pretty normal tho and handling them well/efficiently is the big thing. Using RL for this would be like trying to use graphene to make a computer screen because it's new and flashy and everyone's talking about it. RL is inherently very sample inefficient, and is there to approximate when you don't have certain defined informative components, which we do have in computer vision in spades. Crossentropy losses (and the like) are (generally, IME/IMO) what RL losses try to approximate, only on a much larger (and more poorly-defined) scale.
Please mark speculation as such -- I've seen people see confident statements like this and spend a lot of time/manhours on it (because it seems plausible). It is not a bad idea from a creativity standpoint, but practically is most certainly not the way to go about it.
(That being said, you can try for dynamic sparsity stuff, it has some painful tradeoffs that generally don't scale but no way in Illinois do you need RL for that)
ninamoss
Really appreciated the post, very insightful. We also use VITs for some of our models and find that between model compilation and hyperparameter tuning we are able to get sub second evaluation of images on commodity hardware while maintaining a high precision and recall.
GaggiX
>text in photos, phone screens, diagrams and charts, 448px² is enough
Not in the graph you provided as an example.
yorwba
It has this note at the bottom:
"Note that I chose an unusually long chart to exemplify an extreme case of aspect ratio stretching. Still, 512px² is enough.
This is two_col_40643 from ChartQA validation set. Original resolution: 800x1556."
But yeah, ultimately which resolution you need depends on the image content, and if you need to squeeze out every bit of accuracy, processing at the original resolution is unavoidable.
zamadatix
It's enough, especially if you select one of the sharper options like Lanczos, but 512px is sure a lot easier for a human.
In the Twitter thread the article mentions, LeCun makes his claim only for "high-resolution" images and the article assumes 1024x1024 to fall under this category. To me, 1024x1024 is not "high-resolution." This assumption is flawed imo
I currently use convnext for image classification at a size of 4096x2048 (definitely counts as "high-resolution"). For my use case, it would never be practical to use VITs for this. I can't downscale the resolution because extremely fine details need to be preserved.
I don't think LeCun's comment was a "knee-jerk reaction" as the article claims.