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- Like all of these things, your training data matters and the internet is awash with videos of people climbing badly. A lot of people specifically post "I can't climb this, what am I doing wrong?" videos. World cup climbers are, by the nature of the competition, extremely talented and technically proficient climbers. Even when they fail, they fail in smart interesting ways.
- There's lots of high quality video footage out there. Heck, the problems are even set with visual clarity in mind which would help when parsing that footage. There's potentially enough video to train instances on individual climbers. You could run side by sides like "How would Tamoa climb this and how would Janja climb this?".
- World cup problems are stylistically distinct. They involve lots of moves "typical" climbers will never ever encounter. Many climbers will look at a typical gym problem and think "I have an idea of how to climb this" but will look at a world cup problem and just think "????????". An app that told you how a problem like that should be climbed might be useful.
There are drawbacks too.
- World cup climbers are outliers, whose physical ability (strength, flexibility, etc.) give them access to kinds of movement that other climbers just don't have. No amount of "knowing the sequence" will get me up a climb that requires a full bat hang (look it up) because I just don't have the ankle strength to do the movement.
- World cup "style" is only commonly used at high level comps and in very large commercial gyms. It's probably not extremely relevant to a typical climbing session.
- World cup problems are very hard. Mostly v10 and up? It would be hilarious to watch a model trained on genetic monsters crushing the world's hardest boulder problems try to tell a doughy office worker (me) how to climb v2.
It is ok that it is a solution looking for a problem. There is obviously no 'business' or 'product' here. It isn't like there is a payment link on the page or anything.
What I'd like to see the comments focus on is that we should just be happy that someone is making the effort to learn more about AI and building tooling around it. Experimentation is king.
They've put their work out into the open (soon to be open sourced even!), not to be criticized over whether it is useful or not, but just that they created something that could spawn other interesting things that solve real world use cases.
Huge kudos for doing this work.
Also, this claims that the wall type or video quality doesn't matter, but I have a hard time understanding how the model would be able to understand that a small crimp could possibly be dual textured and therefore has only a few specific ways of approaching it.
So it seems that this is more for visualizing a climb (which is a skill most climbers should develop) and not really for dialing in some sort of microbeta for a problem.
Will attach more example outputs and make a detailed document about how the model was built and the research behind it. If this is interesting to you, feel free to sign up to the waitlist on www.climbing.ai (and make sure to sign up for our Discord!)
I originally planned on open sourcing the model, data, weights, and code. Only a few people (me and a couple friends) have access to the hosted model on the web app.
If enough people are on the waitlist, I will consider releasing access. This is very expensive to run so was only considering open sourcing it.
Note: the model works well sometimes, but most of the times it does not. This is an early research preview. Please tamper any expectations. A really good general model requires millions of videos and much more training time so it is really prohibitively expensive. As mentioned before, if someone has the compute, all they have to do is scale the existing dataset and training pipeline (which I will publish open-source in the coming weeks).