viseron VS darknet

I'm using 4 Dahua turret cameras, also setup exactly like u/o4b describes. Works flawlessly. While I'm also using Frigate, there's another similar piece of software called Viseron that I've just become aware of that looks similar. I can't speak for it at all as I'm very happy with Frigate, but if you're starting from nothing, you may as well consider which may work better for you.

Check out the release notes: https://github.com/roflcoopter/viseron/releases/tag/v2.0.0

Viseron

I have been using zoneminder for years and love it. If you can actually get a tensorflow unit, Frigate can be good. Just heard about Viseron, and am looking at trying it. https://github.com/roflcoopter/viseron

There is also: https://github.com/roflcoopter/viseron - which again, using mqtt. You would need to bridge it to Signal.

I do love having my threadripper in my home docker server though. Home assistant, plex, viseron, etc. all love the available CPU.

If not motioneye, there are a bunch of other free options. I run zoneminder - it should work there with filters to move clips from primary storage on a schedule. You could do it with this one + rclone as well: https://github.com/roflcoopter/viseron

viseron works pretty well for me with cpu only.

YoloV3/4 is C based if that counts: https://github.com/AlexeyAB/darknet

This is all well-known information, see any (and all!) previous discussions when YOLOv5 comes up. For details: https://github.com/AlexeyAB/darknet/issues/5920

2) YOLOv5 is both slower and less precise than YOLOv4. Why use YOLOv5? Source: https://github.com/AlexeyAB/darknet/issues/5920

So my recommendation is you stuck with Darknet/YOLO and use v4 of YOLO. The Darknet framework license is definitely suitable for commercial use: https://github.com/AlexeyAB/darknet/blob/master/LICENSE

Still to long. Here's my favourite license: https://github.com/AlexeyAB/darknet/blob/master/LICENSE

When YOLOv7 was out, I built a web app to test it against the classic YOLOv4 and shared it with my team, then deployed it online to share with the community.

If you're worried about performance, you shouldn't be using YOLOv5 since it is slower (and less accurate!) than YOLOv4. Source: https://github.com/AlexeyAB/darknet/issues/5920

I looked around online but I only found this post discussing a related issue, leading me to think there seems to be some sort of compatibility issue going on here. And I think this is the most recent version of the file I am trying to compile located on the exact same folder where my copy is and when I opened it it shows CUDA 11.1 in line 307.