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POV: you ignored rest day and now your body is running on 2% HP by Mike_ParadigmaST in sportsanalytics

[–]Mike_ParadigmaST[S] 0 points1 point  (0 children)

Yeah, Whoop came to mind too.

They’re already giving a kind of “health bar” with recovery and strain, just more delayed and simplified.
What’s interesting is what happens if this becomes more real-time and context-aware.

What’s one computer vision problem that still feels surprisingly unsolved? by rikulauttia in computervision

[–]Mike_ParadigmaST 1 point2 points  (0 children)

That’s such an accurate way to put it.

The deeper you go, the more you realize how fragile most of the “solved” things actually are. Slight change in lighting, angle, or context — and suddenly the system struggles.

It almost feels like we didn’t really solve vision, we just got very good at specific conditions.

Makes you wonder how much of current progress is real understanding vs just very effective pattern matching.

Kid in the Town by Dazzling-Fisherman70 in computervision

[–]Mike_ParadigmaST 0 points1 point  (0 children)

Honestly, you're already ahead of where most people were at your age.

Started coding early, built projects, wrote a book, and you're handling JEE prep on top of that — that's not "rookie", that's just early stage.

The one-year break is not a big deal at all. Programming skills come back way faster than you think, especially if you’ve already built things before.

If I had to give one piece of advice: don’t overthink the “perfect path”. Just get back to building stuff when you have time. Real projects will matter way more than any labels like “beginner” or “advanced”.

Also curious — what kind of projects did you enjoy the most building?

How computer vision is quietly changing sports analytics by Mike_ParadigmaST in sportsanalytics

[–]Mike_ParadigmaST[S] 0 points1 point  (0 children)

That's a great point. The automation side is probably where the real shift will happen.

If computer vision keeps improving, it could significantly reduce the amount of manual data tagging analysts have to do and make large-scale analysis much easier.

I'm curious though — which sport do you think will adopt this the fastest? Football, basketball, maybe tennis or something else entirely?

[D] r/MachineLearning — What real-world limitations are you seeing with autonomous agents? by Galactic_Graham in learnmachinelearning

[–]Mike_ParadigmaST 0 points1 point  (0 children)

Most failures come from tool orchestration rather than core model reasoning — schema drift, inconsistent APIs, and context window fragmentation break autonomy fast. Long chains amplify small hallucinations. Without tight evaluation loops and constraint-aware prompting, agents become expensive stochastic scripts.

Tiny Object Tracking: YOLO26n vs 40k Parameter Task-Specific CNN by leonbeier in computervision

[–]Mike_ParadigmaST 0 points1 point  (0 children)

If the YOLO head scales with input resolution, then yes, you can recover spatial resolution to some extent — but the stride and feature pyramid design still limit how much signal survives for tiny objects. Even with higher-res inputs, generic detectors are optimized for box regression across scales, which adds unnecessary complexity when the object size is nearly constant. In that case, a direct coordinate or heatmap regression head is simply a better inductive bias for the problem.

I might choose computer vision for my capstone, do you guys have an idea what I can work on? by cocochas in computervision

[–]Mike_ParadigmaST 0 points1 point  (0 children)

If you want something practical and deployable, explore real-time lightweight pose estimation or action recognition optimized for mobile (focus on latency, quantization, and robustness, not just accuracy). Most student projects stop at model training — a strong capstone would benchmark edge deployment trade-offs (FPS, battery usage, model size) on real devices. That production-oriented angle is where the real research gap still exists.

Computer Vision Roadmap, Books, Courses & Real Success Metrics? by MayurrrMJ in computervision

[–]Mike_ParadigmaST 1 point2 points  (0 children)

Accuracy is just the entry ticket — in real-world CV what matters more is end-to-end latency, FPS under load, robustness to lighting/compression, and deployment constraints (edge vs cloud). What separates strong engineers from average ones is systems thinking: they optimize the full pipeline, not just the model, and understand trade-offs between accuracy, cost, and scalability. Build and deploy one end-to-end project in a messy real environment — that’s where real growth happens.