11–20 of 32 posts
Earlier quoted context omitted.
YOLO in this case stands for "You Only Look One".
Yes. The point I was making is that YOLO and v7 don't go well together, and that is true for either meaning of YOLO.
It's not as if this is named "the final algorithm v7"
> the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher Yikes. It's not clear to me if that's the upper limit on accuracy or a limit imposed by requiring that it run at 30 FPS, but still...yikes.
It's clearly the latter and I don't see why it would be "yikes". Real time detectors are useless if "real time" means 1fps.
From the paper:
> For example, multi-object track- ing [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc.
LOL, these are all great use cases for a model with < 60% accuracy!
As someone who got only his feet wet with OpenCV like 20 years ago, so basic shape recognition and no AI involved, what read/software, etc. would you suggest to catch up and play with current technology without being inundated by theory that I'm sure I couldn't grasp?
As someone who got only his feet wet with OpenCV like 20 years ago, so basic shape recognition and no AI involved, what read/software, etc. would you suggest to catch up and play with current technology without being inundated by theory that I'm sure I couldn't grasp?
Go to huggingface.com and start with some of the tutorials. The operational/engineering skill sets alone are all you need to treat modern ML models like any other black box API/SDK.
Earlier quoted context omitted.
It's clearly the latter and I don't see why it would be "yikes". Real time detectors are useless if "real time" means 1fps.
What good is speed if the accuracy isn't significantly better than a coin flip? From the paper: > For example, multi-object track- ing [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc. LOL, these are all great use cases for a model with < 60% accuracy!
Earlier quoted context omitted.
It's clearly the latter and I don't see why it would be "yikes". Real time detectors are useless if "real time" means 1fps.
What good is speed if the accuracy isn't significantly better than a coin flip? From the paper: > For example, multi-object track- ing [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc. LOL, these are all great use cases for a model with < 60% accuracy!
Because distinguishing an object as belonging to one class out of a thousand with 50% accuracy doesn't mean it's a coin flip. You'd need a thousand-sided coin. Random chance in that case is 0.1%, which maeks 50% way, way better.
As someone who got only his feet wet with OpenCV like 20 years ago, so basic shape recognition and no AI involved, what read/software, etc. would you suggest to catch up and play with current technology without being inundated by theory that I'm sure I couldn't grasp?
Here's a good resource: https://eli.thegreenplace.net/2018/depthwise-separable-convo....
1. Intro - a note on the overall problem domain - object detection in this case and bit zoomed in to the DL space. 2. Related work - work so far in the domain .. without critizin it. 3. Problem statement - what is the knowledge gap in the related work this paper is talking about. 4. Solution - how did we address the gap. 5. Validation - how do we claim our solution addressed the gap it was intended to address.
This paper's abstract covers only the last part and sporadically a bit of 2. What I want to know is this abstract is "what is the new learning in the yolov7 arch?"
Perhaps the bigger picture here is that it points to metrics chasing as a proxy for a "research agenda" in the ML community.