When I use Codex/Claude to complete a computer vision task, such as extracting assets from an image, OpenCV is their default solution. However, I believe that using YOLO and other methods is outdated. The best solution now is to directly use Nano Banana or other AI image models. A paper has proven that image generation models can perform most CV tasks well. I believe the new OpenCV should become a wrapper for VLM or…
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.
A few years ago I was using OpenCV is a commercial Android SDK (it might still be being used; also because iOS provided almost all of those "needs" ready-made and Android just didn't, neither did Firebase, or Jetpack suites/tools). I was the one who had added it in the SDK. There was a lot I/we could do but as an Android developer (barely any exposure to CV or even C/C++) what I felt we lacked was documentation, a community. We struggled with even shaving off parts that we did not want to ship with our SDK. Speed was such an issue. The problem was someone who just wanted to use the lib (on mobile) a lot of things felt esoteric and out of reach i.e difficult. It didn't have to be.Sadly LLM wasn't at full speed back then, barely useable, not even talked about. Something like this would have been a perfect use case of AI/LLM. A coder, not from the exact/specific field the tool was made in/from, but being able to take full advantage of its capabilities in a nuanced/selective manner.
> One practical detail is worth knowing. The new engine is CPU-only at the moment, so if you select a non-CPU backend and target (for example CUDA or OpenVINO through setPreferableBackend and setPreferableTarget), you will want the classic engine. So there's room for even better performance!
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.
You can use ONNXRuntime with a TensorRT backend, so one does not exclude the other.
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 worl…
The release post is AI-written with little human oversight and it shows.
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.
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 worl…
The release post is AI-written with little human oversight and it shows.
I had to stop reading after: "This is not just another incremental release. OpenCV 5 is a major step forward."
If a human can't be bothered to write a piece, I can't be bothered to read it.
They’re multimodal LLMs trained for image generation. Turns out that if you want to generate images you gotta know what things look like.
That's not helpful my brother. If you have details share them, if not, don't pretend you are more illuminated than me. Is the image(text) function reversible? Or are they brute force searching a nearest neighbor like word2vec/hash brute forcing.
Google recently released their paper "Image Generators are Generalist Vision Learners" about exactly this. They fine tuned Nano Banana pro into what they call Vision Banana which can do segmentation etc.