roflcoopter

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. v3.0.0 Has finally been release which features a lot of improvements, including 24/7 recordings with a nice timeline view Check out the release notes: https://github.com/roflcoopter/viseron/releases/tag/v3.0.0 Viserons features include, but not limited to the following: Continuous (24/7) recordings Tiered storage, allowing multiple storage media with different retention policies A timeline view of events Built in authentication system Object detection via: YOLOv3, YOLOv4 and YOLOv7 Darknet using OpenCV Tensorflow via Google Coral EdgeTPU CodeProject.AI Motion detection Face recognition via: CompreFace CodeProject.AI dlib Image classification via: Tensorflow via Google Coral EdgeTPU License plate recognition via: CodeProject.AI Responsive, mobile friendly Web UI written in TypeScript React MQTT support Home Assistant MQTT Discovery Lookback, record before an event actually happens 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 Telegram support for notifications 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. A few screenshots are included below, please go to the documentation for more: https://viseron.netlify.app/docs/documentation#screenshots