It's certainly a choice to make your headline feature a new ONNX engine, feature a bunch of comparisons how it's better than ONNXRuntime, while casually mentioning on the side that the cool new much faster engine is CPU-only

Sure, running models on the CPU is very much a thing in computer vision (the benchmarked YOLOv8n has 37M params). But this whole announcement feels more like OpenCV catching up to the modern world, not "The Biggest Leap in Years for Computer Vision"

Still great, needing fewer libraries is a good thing, but maybe a bit oversold

No one uses ONNXRuntime (nor the new engine in OpenCV 5) in production. For anything performance-sensitive, one would run models under TensorRT, as an example.

In pixels/second? Sure!

Whenever you can run a model like Nano Banana or other vision-LLM with the same compute and time performance/restrictions as an OpenCV or YOLO call, you can make that comparison. Until then, I would not call YOLO and OpenCV outdated, it's simply wrong. There's a time and place for big V-LLMs just as there is a time and place for more "traditional" computer vision methods.

I can get great results from a YOLO model with 30M to maybe 300M params. To get decent CV from a LLM 8B params is the absolute minimum, closer to 30B for interesting tasks

I might be on board about LLMs being the future of OCR (though many would disagree), but for general CV they are very inefficient for very limited benefit

I've built hardware with a pi zero 2 + pi cam running a mildly fine-tuned YOLO doing local-only object detection as a USB-OTG device, in a use case where any off-device API calls would have been totally unacceptable, and where the object detection was part of the human interaction loop with a hard ceiling of 300ms on the total interaction time of which the object detection was only one process among many.

We're not going to fit Nano Banana or anything like it on a device with 512MB RAM and a GPU old enough to be irrelevant, and again, API calls just aren't on the menu.

That is a very uninformed view. Real time CV is not going to be doing that anytime soon.

Great, let me know when those models can run on-server and process/analyze streams of ID images with less than 100ms of latency. You’ll need to make sure you have a massive set of training data including all manner of slightly blurred and slightly distorted ID cards

do you realize how many edge or unconnected nodes do OpenCV work?

some SBC w/ an industrial camera that is doing pick-place or go/no-go operations on a conveyor belt against a singular object type doesn't need a huge image-gen/llm model governing it.

I mean have you even considered the kind of performance an opencv function can get w/ just mask-matching? I mean even with a fancy YOLO model these answers get thrown out in 1.5-50ms ; this is just a wholly different time scaling.

100.000 pictures take a lot of time with LLMs.

Its a lot better, faster, cheaper to use LLMs for initial labeling together with hand finetuning and then training YOLO with this.

Training YOLO takes a few hours and is then very fast.

"When I use..."

Dude, in business we think in terms of large numbers, internationally easily in billion times processing images. This wouldn't cut it.

Also, do you buy the mega expensive super individually designed shoes from the best shoemaker there is to march along though some dirt or simply stick to gumboots?

OpenCV is used behind the scenes for many of the fancy stuff those major AI provider pretend to do. Claude is a huge system and not a LLM anymore.

If I want to identify and measure the size of round things in my orange sorter machine, I shouldn't have to resort to an unnecessarily complicated solution just because some AI bros can't understand that not everything needs to be an AI model.

Like, the AI model tools already exist, all that would be accomplished if OpenCV pivoted would be to take it away for people who want to do low-level vision programming. It wouldn't add anything useful to the world, just destroy an excellent library.

I am confused, how can functions that output images help with functions that should take images as input?

YOLO has basically solved that for my use cases for a couple years now. If you want labels that are not in the pretrained labels it's also easy to fine-tune, provided you're willing to label 200 or so images

If you need something less restricted to existing labels (say wanting all the red apples, or all cardboard signs) SAM3 is great, as the sibling comment says

That seems to be the way things are going.

Large general models have taken over in NLP, and (outside of embedded/low latency applications) it seems like they are coming for CV next.

So you should soon be able to have large generic model that can detect whatever for you.

It's already pretty much possible with open-vocabulary detectors like SAM3, where you could just prompt it with "Apple": https://ai.meta.com/research/sam3/

moondream is a beast