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Embedded systems can make network calls to powerful, GPU equipped servers.
Sure. Claude does that. "Cogitated for 1m 50s" doesn't work for real-time applications.
They really improved the performance. I tested yolov8 medium segmentation model on intel i7 11th gen cpu. Opencv 4.11 : ~255ms Opencv 5.0.0 : ~185ms with the same code.
AI written release post and it shows...
Indeed. Well written, clear, informative and to the point.
As for being well-written, does that refer to correct use of grammar and no typos, or do you mean that you find that bots write better than humans in any other way?
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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.
Curious on what backs this assertion. As a counterpoint we’ve been running 200+ models in production for more than 5 years - language models, embedding, classifiers, low tens to hundred M params. Traffic in the order of 1-2M requests/day and everything is enabled by onnx with some cgo (or Rust) plumbing on top. What’s your SLA?
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The diagrams definitely look like LLM output as well
The diagrams were generated with Nano Banana Pro (most probably, or alternatively with ChatGPT Image 2), if you look closely in high contrast areas you'll see artifacts in the background that give it away. I personally don't mind AI generated content when it's properly reviewed, but unfortunately more often than not the author just glances at the result and decides it's good enough. Example: https://opencv.org/wp-con…
The thing I love about OpenCV is that it remains hands down the best library for simply loading images and video. I've never even used any of its fancy computer vision features, but if I need to load a video file and look at the pixels - which I did need to do recently for an art project - OpenCV does it in about four lines of code.
Done a few projects with OpenCV over the years, and I agree it can be fun. However, it has a few issues: 1. Patented algorithms that are effectively impossible to license in a commercial setting. 2. Permuted API that change how identically named functions behave over versions. 3. Hardware CUDA version coupling deprecating support every major release. 4. Inconsistent and contradictory documentation in the constant sub…
But I can’t really complain because it’s open source and added to by contributors.
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Indeed. Well written, clear, informative and to the point.
It could be the best written, most informative article they've ever read, but anti-ai folks would dismiss it as slop the moment someone told them it was written by ai.
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Sure. Claude does that. "Cogitated for 1m 50s" doesn't work for real-time applications.
You can submit many queries in parallel to increase throughout. Smaller models and faster hardware can reduce the time per query too.