roflcoopter

If your host is setup correctly all you need to do is pull the roflcoopter/amd64-cuda-viseron image and it will use your GPU automagically if it is detected properly. I fully agree that the config should be editable in the GUI and i plan to do that at some point, but it is a pretty huge amount of work so i cant give a timeline on that sadly. Viseron v3 is in beta right now which brings a lot of improvements however, like 24/7 recordings

Seems to be working fine here right since it is recording? Or are you using CPU detector here?

Can you restart Viseron and paste the full log? It should give a hint of what's going on. Thanks for the heads up on the icon!

@parish99Sorry for not responding sooner, glad you figured it out

VA-API not working often boils down to permission issues. Does it work if you run with PUID=0 and PGID=0 and/or privileged? What do you mean by recordings are stored with random names? Could you show an example? They should be stored in a separate folder per camera/date with a timestamped file. The error in your log is simply the object detector which takes some time to initialize the first time, nothing to worry about!

Yes it is normal, object detection loads the model into memory, you can reduce it by using a smaller model but at the cost of accuracy

Okay great, thanks for confirming! I will look at the icon issue.

Updated the template, but i think it is broken now. Can anyone assist in reviewing the template here: https://github.com/roflcoopter/viseron-unraid-ca-template/blob/master/Viseron.xml and see whats wrong with it?

Absolutely, will try to fix. However i do not use Unraid so i am not really sure what to change, the template was created for me by a user. I can make some proposed changes to the git repo, would you be able to help review it?

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

I suggest you check out this PR: https://github.com/roflcoopter/viseron/pull/306 I am currently rewriting Viseron in that PR, and it is almost done. The documentation is the only part missing. That PR has a small config example that you should be able to use. You need to pull the Docker tag called `modularized` in order to get Viseron v2 however, not sure how you do that using Unraid

Viseron supports this with the roflcoopter/viseron-cuda container, however i am not sure how to incorporate that into this implementation. I am not using Unraid myself so i dont know how to do that unfortunately.

Viseron will first try to load the EdgeTPU from USB, if it fails PCIe will be tried. I think you need to mount the coral into the container like this but I am not sure --device /dev/apex_0:/dev/apex_0

Well right now there isnt much to show, videos are recorded when configured objects are detected. A GUI will be implemented in the future where you can view cameras/recordings and maybe even edit the configuration

Application Name: Viseron - a self-hosted NVR with object and face detection Application Site: https://viseron.netlify.app Docker Hub: https://hub.docker.com/repository/docker/roflcoopter/viseron Github: https://github.com/roflcoopter/viseron Viseron Viseron is a self-hosted NVR deployed via Docker, which utilizes machine learning to detect objects and start recordings. v2.0.0 Has finally been release which features a lot of improvements, including a fresh new frontend interface Check out the release notes: https://github.com/roflcoopter/viseron/releases/tag/v2.0.0 Viserons features include, but not limited to the following: - Object detection via: - YOLOv3, YOLOv4 and YOLOv7 Darknet using OpenCV - Tensorflow via Google Coral EdgeTPU - DeepStack - Motion detection - Face recognition via: - dlib - DeepStack - CompreFace - Image Classification - Responsive, mobile friendly Web UI written in TypeScript React - MQTT support - Home Assistant MQTT Discovery - Lookback, buffers frames to record before the event actually happened - Supports hardware acceleration on different platforms - CUDA for systems with a supported GPU - OpenCL - OpenMax and MMAL on the RaspberryPi 3B+ - video4linux on the RaspberryPi 4 - Intel QuickSync with VA-API - NVIDIA video4linux2 on Jetson Nano - Multiplatform, should support any amd64, aarch64 or armhf machine running Linux. Specific images are built to support: - RaspberryPi 3B+ - RaspberryPi 4 - NVIDIA Jetson Nano - Zones to limit detection to a particular area to reduce false positives - Masks to limit where object and motion detection occurs - Stop/start cameras on-demand over MQTT I hope you'll find this useful! Viseron is a project that is under active development and I appreciate any feedback or feature requests you have.