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In computer vision transformers have basically taken over most perception fields. If you look at paperswithcode benchmarks it’s common to find like 10/10 recent
by CHY872 11mo ago
In computer vision transformers have basically taken over most perception fields. If you look at paperswithcode benchmarks it’s common to find like 10/10 recent winners being transformer based against common CV problems. Note, I’m not talking about VLMs here, just small ViTs with a few million parameters. YOLOs and other CNNs are still hanging around for detection but it’s only a matter of time.
- thesz 11mo agoCan it be that transformer-based solutions come from the well-funded organizations that can spend vast amount of money on training expensive (O(n^3)) models? Are there any papers that compare predictive power against compute needed?
- nickpsecurity 11mo agoYou're onto something. BabyLM competition had caps. Many LLM's were using 1TB training data for some time. In many cases, I can't even see how many GPU hours or what size cluster of what GPU's the pretraining required. If I can't afford it, then it doesn't matter what it achieved. What I can afford is what I have to choose from.