Great to hear.
OpenCV was so easy and smooth to set up for doing tasks like generating thumbnails from uploads from arbitrary photo uploads regardless of format (including funky new formats like webp, avif, or heic).
OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
121–130 of 160 posts
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#122AI written release post and it shows...
Yeah, blatant "it's not x, it's y":
> This is not just another incremental release. OpenCV 5 is a major step forward.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#123Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#124curious how many people model killed on battlefields of ukraine and russia.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#125Earlier quoted context omitted.
It can offer a ton of user value. There is a whole industry built upon this idea, Internet of Things.
IoT wasn't not built on "send all the data off to a hosted GenAI". It predated them by quite a few years.
The GPUs were doing video transcoding instead of GenAI.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#126Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#127Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#128Earlier quoted context omitted.
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
> provided you're willing to label 200 or so images A quick note to say that this is also a task you can hand to things like gemini.
Yep- this is what I do. I use a high quality VLM to generate labelled boxes (in my case, around tardigrades in a microscope image), do some light editing to fix the small number of errors, and then train YOLO26 with it. Works great, saved me tens of hours of labelling. It's a bit scary that there is a VLM that works as well as my fine-tuned model (although much slower).
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#129Earlier quoted context omitted.
IoT wasn't not built on "send all the data off to a hosted GenAI". It predated them by quite a few years.
The GPUs were doing video transcoding instead of GenAI.
You can run OpenCV on a GPU-less Raspberry Pi or other IoT device just fine.
And most IoT devices aren't doing video transcoding at all. You're making some very odd assertions in this thread.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#130Earlier quoted context omitted.
> provided you're willing to label 200 or so images A quick note to say that this is also a task you can hand to things like gemini.
Yep- this is what I do. I use a high quality VLM to generate labelled boxes (in my case, around tardigrades in a microscope image), do some light editing to fix the small number of errors, and then train YOLO26 with it. Works great, saved me tens of hours of labelling. It's a bit scary that there is a VLM that works as well as my fine-tuned model (although much slower).
thats a fantastic strategy thank you, and thanks to all the other helpful posters as well here. do you have any tips for how to choose the base yolo model? or just any generic one will do?