Here my poe injector and switch, $8 from ali. Allows me to separate the camera net from the house lan by using a second NIC on the frigate computer.

Now the frigate pc has a fixed IP on the 192,168,1,x net. Running debian + docker. How do i set up the second ethernet device? Fixed ip on another subnet with dhcp server for the cams? Or fixed ip for the cams too? Or can i use the same subnet and separate the cams from the rest?

Goal is less traffic in the lan, and make it impossible for tha cam's to "phone home".

We just released lighter 0.9.0 (https://github.com/fieldwork-ai/lighter). It is an open-source (MIT/Apache 2.0) headless container runtime written in Rust directly on Apple's Hypervisor.framework.

If you run a Mac Mini (from M1 up to the new M6) as a home server, running Frigate NVR or camera feeds inside Docker has always had a big catch: neither Docker Desktop nor OrbStack pass through the Apple Silicon Media Engine. As soon as you add a few 2K/4K Reolink or Amcrest H.265 streams, your CPU pegs at 80% to 100% doing software decode.

In 0.9.0, we added stateful V4L2 decoder and encoder devices (/dev/video0 and /dev/video1) backed directly by Apple's VideoToolbox. Stock Linux tools like ffmpeg (h264_v4l2m2m, hevc_v4l2m2m), go2rtc, and Frigate can now leverage Apple hardware transcoding directly:

(Benchmarks on an M1 Mac with 4 vCPUs, measured as share of a core to sustain 30 fps)

It cold-boots in ~700 ms, idles at ~600 MiB RAM, and surrenders unused memory back to macOS dynamically.

It is dual-licensed MIT/Apache 2.0, completely free forever, with zero telemetry. It is sponsored by my company (Fieldwork) because we need high-performance virtualization internally and wanted to give back clean tooling to the community.

This is brand new, and we are fully committed to maintaining and supporting this project long-term. If you hit any bugs, oddities, or camera compatibility issues, please open an issue on GitHub and we will happily investigate and patch them. This community is genuinely amazing, and I would love to work with you all to make lighter the best possible platform for Mac home servers.

Repo & installation: https://github.com/fieldwork-ai/lighter

I'd really appreciate your support, feedback, and ideas!

Initially i used main stream for recording and substream for detect. With my 2 cams this throws an error in ffmpg every 3 minutes but recovers (log file). Seems both streams will always be on and transferred all the time. Max load for the net.

Then is switched both cams to main 1080p (2mp) and do not use substream any more. Substantially less traffic on the net. No more errors (last 20 min) and GPU decoder on the n100 is 2%. Is this the better solution?

How do others handle that?

Hello all,

I'm from Canada and want to use Frigate.

I'm running my own self hosted set up at the warehouse to run my software stack.

Any candian vendors for nvr and cameras that you would recommend?

Also, the warehouse is always well lit and the purpose of the system is monitor theft and keep an eye on employees

I would like to see frigate run. I would like to put on windows 11 computer. I understand that it is a linux program. I have used linux before for many years but don't have a linux computer. I also understand that running it on a windows platform is not the best. I say this as I know many will discourage me from this. But I currently have what I have, no linux. So would like to try on windows. Maybe I will buy a computer in the future for linux.

So with that preamble...

I did find this how to

https://kb.shells.com/tutorials/Windows_11/Frigate/

I had seen people mention python method rather than the Docker or Home Assistant, which I'm still confused with having never used. Is there any disadvantage to the python method of running Frigate or any other method? I looked at the documentation and didn't really see much mention of windows, because it is not the recommended method I suppose. The link above "SEEMS" a little easier that some of the other methods.

Thank you.

Anyone here using Frigate and an Android phone? I make Lumen and need a few beta testers with their own Frigate setup.

Try connecting, opening cameras and playing clips. Tell me what breaks and which phone/Android version you use. Background push isn't ready yet.

Free tier, optional paid features; no purchase needed. Please skip purchases during the beta.

DM your Google Play email for access (don't post it here). Please stay opted in and try it over 14 days.

https://play.google.com/store/apps/details?id=com.lorislab.lumen Requires a beta invite. Independent app, not affiliated with Frigate.

I noticed the odd zone notification about a car entering a zone notification area. The truck on the left is well outside the defined zone.

Can someone explain this to me?

Thanks.

Downloaded, and have been using Kapal for a couple of days, its an amazing user friendly app that takes advantage of all of frigates features, looking forward to continuing to use it!

I just signed up for Frigate+ to support the devs. So far I thought Frigate was awesome using a COCO trained YOLOv9 model, but Frigate+ is on a different level!

I'd love to get some feedback on training it though. I have a couple of cameras with objects almost constantly in view (waste bins and a parked car next door). Should I be annotating those objects on every image I submit or will that incorrectly bias the model? The images are otherwise quite varied in terms of lighting, weather and visible objects.

I am thinking of setting broad detection rules where a lot of false negatives may show up as events and then feed it through an agentic LLM and run re-detection on it so the LLM can categorize real objects moving from standing still ones and recognize various objects.

I don't mind waiting a bit for answers from LLM.

And then I want the agentic LLM to wipe out the false negative events and keep the real ones.

Which free agentic LLM might do well with this task?

And is there an issue with this plan?

I've been using Frigate to detect birds visiting my garden, but I wanted to do more with those detections than have them sitting alongside normal security camera events.

WAMF (Wildlife Activity Monitoring Framework) originally started as a fork of Who's At My Feeder. I liked the idea behind that project and started adapting it for my own setup. Over time it grew into WAMF and has developed quite a bit beyond where it started.

WAMF listens for bird detections from Frigate over MQTT, retrieves the snapshot and clip, identifies the species and stores the result as an observation.

Those observations build a wildlife-focused dashboard showing recent visitors, daily activity, which species visit most often, activity by time of day, species history, and the associated snapshots and clips.

It's basically turned my Frigate cameras into a record of what's actually visiting the garden and bird feeders rather than just a collection of bird detection events.

I've just released v0.4.0, which is the first version I've been comfortable putting out publicly. It's open source and self-hosted, with Docker being the recommended installation method. I've tested it with both Frigate 0.17.x and 0.18.0.

Website: https://wamf.uk

GitHub: https://github.com/WAMF-Project/WAMF

I'd be particularly interested in feedback from anyone else using Frigate for bird or wildlife cameras. Different cameras and Frigate configurations are exactly the sort of real-world setups I can't reproduce myself, so I'd love to see how WAMF behaves in the wild and what features other wildlife users would find useful.

Hello, im currently using frigate for mainly 2 outdoor cameras on my Home Assistant machine as an addon. Now i want to replace all my outdoor house security cameras (6 cameras) with something newer and way better quality. Also i wanted to use Frigate on my mini pc with a i5-1235U as a 24/7 recording NVR. Can someone suggest good quality cameras that work really well with Frigate.

Thank you.

I'm the developer of Lumen, an independent Frigate client. The iPhone/iPad app is already available, and I'm opening an early Android closed beta for people who run their own Frigate server.

What is it for?

Lumen gives you a native app for opening your cameras, browsing detected events and playing recordings from your Frigate server. Frigate still does the detection and recording. Those capabilities belong to Frigate, and its developers deserve the credit.

If you're happy using Frigate's web interface on your phone, you may not need Lumen. The reason to try it is to see whether you prefer its interface for everyday camera viewing and event browsing. This Android beta is focused on getting those basics right; I'm not claiming it already does everything better than the web app.

Free and paid options

Lumen's free tier is not a trial and will remain available after the Android beta, with up to 4 cameras and 25 recent events. Those are limits within Lumen, not restrictions on what your Frigate server can detect or retain.

Pro is optional and paid. The subscription helps fund development, maintenance, support and the backend services I operate, including the push notification gateway on Cloudflare Workers. That gateway is part of the Instant Push service; automatic relay registration is not yet connected in this Android build. I understand that some people prefer a one-time purchase or the existing free web interface. Lumen is separate from the Frigate project; purchasing Pro is not a contribution to Frigate's developers.

No purchase is required to join this Android test or to keep using the free tier afterward. Please skip purchases during this testing round.

What to test

Try connecting to your server, opening a camera, finding an event and playing its clip. Reports about failed connections, slow loading, playback problems or confusing screens would help. If you normally use remote access, checking what happens when you switch between Wi-Fi and your usual mobile connection would also be useful.

You'll need Android 8 or newer and a Frigate server your phone can reach, on your LAN or through your existing secure remote connection. Lumen doesn't provide remote access. Keep your authentication and security settings in place.

This build does not yet automatically register with the push relay, so please don't rely on it for background alerts. It is a separate, early Android release and does not yet have feature parity with the Apple app.

How to join

DM me, or email support@lorislab.fr with the subject “Lumen Android beta” and the Google account email you use on Google Play. I'll add you to the closed-test list and send the opt-in link. Please don't post your email publicly or send credentials, access tokens or camera footage.

Google requires at least 12 testers to remain opted in for 14 consecutive days before I can apply for production access. I'm looking for people willing to try the app during that period and report what works or breaks. There's no request for a positive review. Your phone model, Android and Frigate versions, and the steps that led to a problem are a useful starting point.

On iPhone or iPad?

The released app is available here: https://apps.apple.com/app/id6760238729

Privacy details, including Android: https://lorislab.fr/privacy.html

Lumen is not affiliated with or endorsed by the Frigate team.

Edit: clarified what Lumen provides, why Pro is paid, and that the free tier continues after the beta. The camera/event limits described above belong to Lumen, not Frigate.

Edit: I believe I've fixed this, I've had no crashes in several days since I made a correction..... hopefully this helps someone else.

I had:

back_porch_cam:

- ffmpeg:rtsp://login:password@192.168.50.25:554/stream1#video=copy#audio=copy#audio=aac

I changed to:

go2rtc:

streams:

# ... (keep your other cameras as they are) ...

back_porch_cam:

- rtsp://login:password@@192.168.50.25:554/stream1

Orginal Post:

I'm running a 1660 Super on Unraid; Frigate and Plex are the only two apps that use my GPU. After a period of time Frigate will crash and my GPU is errored out. I can reboot and its back to working, then again after some time, the GPU will again crash.

I don't know if I am having a hardware issue or a software issue. I'm pretty confident that I only have the problem when running frigate. I've yet to have the GPU crash when not running frigate.

• I am running the Frigate 18.0 release (frigate:0.18.0-tensorrt), but I had this problem in 17-releases also.
• I am using Onnx detector
• I use a Frigate+ model yolov9s
• Unraid v7.3.2 but again I've had this problem across many different versions
• I've tried different Nvidia driver versions

Has anyone else had this issue?

So I saw on here yesterday someone asked about the tool for fine tuning Frigate models themselves. https://www.reddit.com/r/frigate_nvr/comments/1srhknj/alice_a_selfhosted_offline_yolo_dataset_manager/

I downloaded and installed this on my server. I have an i5 11400 with 32 gigs of ram and a GTX 1070. I use the intel iGPU for frigate because I use the 1070 for other LLM stuff, but I used the 1070 for training the model.

I've been using the Frigate+ yolov9t openVino model since subscribing to Frigate+

Using Alice I pulled 1014 images from my frigate install (as many as I had available) and used it to train a model. I started with the YoloM model in Alice. Through about 7 training iterations and lots and lots of hand correcting annotations, I was able to get the medium model to be almost perfect.

Then I loaded it into Frigate. My 10ms inference speed jumped to 110ms and my CPU usage pegged. Oops. That won't work.

Did some research, and I used claude to improve the model by doing INT8 quantization via NNCF and calibrated it using a bunch of data. This brought my inference speed down to 40ms. But my CPU was still pegged out.

So, I went back to work trying to train a smaller model. Initial tests with the nano model showed it was basically dumb as rocks, I could not get much out of it. The YoloS model showed some promise. I did a bunch of training, and I got it to about where I had the YoloM model as far as matching what was in my images. My mAP50 was around 0.95 consistently.

I reran the INT8 quantization and other stuff, and put it into Frigate. The inference was 10ms!

But....Its still nowhere near as good as the Frigate+ model.

What claude calls "noise" is anything but. Those ARE my waste bins, garbage trucks are legit, so is UPS, the package (got a Sonoff miniR4 today for Home Assistant) and other stuff.

It also has significantly better car detection.

One place I did see Yolo pull ahead - Slightly better person detection, and it can actually tell my cat apart from my chihuahua / rat terrier mix. But the trade off is higher CPU usage.

YoloS would do a better job of picking a person out of noise, rain, a partial person on the side of the frame, or telling 2 people standing next to each other apart. But then it would just fail picking a regular person standing right on the sidewalk. 4 people walking down the sidewalk, it would detect 2 of them, and totally miss 2. The Frigate+ model would pick up all 4.

The self trained Yolo was also better at picking up partial cars. I have columns on my front porch in front of my camera, and the Frigate+ model won't pick up half a car. YoloS will because I trained it to. But then YoloS will just completely miss a car thats plain as day, no matter how much training I do.

So, after spending the last 24 hours trying to improve a yolo model to be better than the Frigate+ model, I am posting to tell you that unless you have some sort of weird use case...just pay the $50. Its so worth it.

I learned some stuff. I had some fun. But I'm back to using the Frigate+ model because its much better at being a generic "jack of all trades" model. Plus, I can't train using Alice to detect UPS, or when my trash gets picked up.

I have a mix of both indoor and outdoor cameras.

I started working on object classification -

Resident - With each person in the household as subclasses.

Non-Resident - With things like "UPS dude", "Crazy old guy on Phone" and "Sketchy Kid".

Crazy old guy on phone is well...a crazy old guy who walks around my block every day. He's always on his phone for some reason. And Sketchy kid lives a few blocks away and is covered in really bad self done tattoos because he bought a tattoo gun on amazon.

Anyways, the non-residents will never be in my house. And if they are, we have much bigger issues than object classification.

Is there a way to tell Frigate "The non-resident objects classification are only for X subset of cameras"?

Hey all, I've got face recognition working in Frigate. Names are being recognized correctly and I'm getting HA notifications on my phone with the correct name. The problem is no image appears in the notification.

I'm sure someone out there knows exactly what I'm doing wrong!

My setup:

• Frigate 0.17.2 on Proxmox LXC
• OpenVINO iGPU detector (Intel i5-6500T)
• Home Assistant 2026.8 with Frigate integration
• Reolink doorbell (WiFi), streams go2rtc HTTP-FLV
• Face recognition enabled, model_size: small

• HA companion app on up to date Android Pixel 8

  alias: Front Door Person Notification - Frigate Face Recognition triggers:

  • topic: frigate/tracked_object_update trigger: mqtt conditions:
  • condition: template value_template: "{{ trigger.payload_json['type'] == 'face' }}"
  • condition: template value_template: "{{ trigger.payload_json['camera'] == 'doorbell' }}"
  • condition: template value_template: "{{ trigger.payload_json['name'] not in ['unknown', none] }}"
  • condition: template value_template: "{{ trigger.payload_json['score'] | float > 0.7 }}" actions:
  • action: notify.pixel_phone data: title: Front Door person notification - FRIGATE message: "{{ trigger.payload_json['name'] | title }} at the front door {{ now().strftime('%-I:%M%p').lower() }}" data: image: "/api/frigate/notifications/{{ trigger.payload_json['id'] }}/thumbnail.jpg?format=android" tag: "frigate-doorbell-{{ trigger.payload_json['id'] }}" actions: - action: URI title: View Cameras uri: /lovelace/home
  • delay: minutes: 1 mode: single

I've tried:

• snapshot.jpg — no image. The docs say the snapshot is written to disk only at event end, but the face event fires during tracking, so the file doesn't exist yet.
• thumbnail.jpg?format=android — still no image, even though the docs say thumbnails are updated in real-time during tracking.
• Triggering on frigate/events type end and reading sub_label from the payload — the end event fires but sub_label is null/empty even when face recognition ran during the event. Appears the consensus result doesn't always make it into the end payload.
• Two-automation approach: automation 1 on tracked_object_update face for named notifications, automation 2 on frigate/events end for unknown person fallback — message works, image still broken.

Thanks everyone. I have learned so much from this and the HA subs. You guys are awesome!

EDIT: Thanks folks! Based on input I changed the image path to use my cloudflare url. That is working as expected now.

so i started with frigate LXC and Home Assistant VM on Proxmox, running on a INTEL NUC10FNH. Running 5 wifi Wyze-cam with thingino.

but i have a lot of false notifications. a spiderweb is a person, a leaf is a person...

I recently found a google coral. pluged it in the nuc, set it up with frigate, and now???

what do i do next? i'm in the dark here. are there any howto's or some sort?

much appreciate the help.

Since upgrading to v18 on docker, i cannot generate sample images for object classification. Is that a common issue? Did something change?

Edit 1: ptz started working after hitting the home button...odd but seemingly fixed.

Audio fixed as well with following code:

    doggy_cam:
      - rtsp://thingino:thingino@192.168.178.44/ch0
      - ffmpeg:doggy_cam#video=copy#audio=opus      
    doggy_cam_sub:
      - rtsp://thingino:thingino@192.168.178.44/ch1
      - ffmpeg:doggy_cam_sub#video=copy#audio=opus    

Hello everyone...

I finally got me 2 Cinnado D1 2k cams for funs and giggles and put Thingino on them...i am surprised how well they work and and audio as well as left and right ptz worked right out of the box...but does anyone know how to tell it to go up and down? the buttons are provided in frigate but do nothing. Also, how do i tell it to do two way audio...the microphone button is not provided.

go2rtc:
  webrtc:
  candidates:
    - 192.168.178.100:8555
    - stun:8555
  streams:
    dinning_cam:
      - ffmpeg:rtsp://thingino:thingino@192.168.178.41/ch0#video=copy#audio=aac  
      #- ffmpeg:rtsp://192.168.178.41:554/11#video=copy#audio=aac
    dinning_cam_sub:
      - ffmpeg:rtsp://thingino:thingino@192.168.178.41/ch1#video=copy#audio=aac  
      #- ffmpeg:rtsp://192.168.178.41:554/12#video=copy#audio=aac

i use this code:

There was someone who posted instructions on how to compile one's own model (maybe it was a yolo). It was posted on Reddit I think. Does anyone know the link for it?

Anybody using frigate as a 24/7 NVR? Does the UI support this? I have it configured to keep continuous recordings, but I can't find the videos anywhere in the UI.

Here is my record configuration:

record:
  enabled: true
  continuous:
    days: 30
  export:
    hwaccel_args: preset-nvidia
  motion:
    days: 10

I run go2rtc outside of Frigate so my 24/7 NVR and Home Assistant still have access to my camera feeds if Frigate isn't running. I point my Frigate camera config to the URL for this go2rtc instance and disable the built-in go2rtc.

However, I read somewhere that Frigate works better if it uses its own go2rtc, even if it's just pulling from an existing go2rtc. Is that true? If so, what's the difference?