Hello...

i am wondering...since i get so much better inference time with a coral and yolo9 compared to the n100 igpu.
is there actually better detection with the graphics compared to the coral that i have missed or is it just that the coral is limited in any way?

Before my dog ate it, i had 13 ms on the coral and over 50 ms on the igpu.

The coral uses the yolov9s-relu6 320x320 and the igpu uses yolov9t 320x320

So in my understanding the coral is using the more capable model and gets better inference.

Would love to hear any info about it...thx

I am trying to pass cars and number plate data to HA.

All person detection works as far as I am aware however Frigate is detecting 3 cars in my carpark which is correct but HA says there are no cars despite showing the image for last car detected.

Any advice?

Hey we all need to start somewhere🤣

couple months ago i picked up a Lenovo M920q. got proxmox and home assistant running, absolutely mint.

recently installed a reolink poe doorbell, and an ip camera at the side of my house. im an electrician so i have no knowledge of all this…. slowly picking it up

is there an up to date guide start to finish?

all very confusing for me so bare with me🤣

thank you in advance for your help

Hi everyone! Looking into Frigate and am curious where people get Coral TPUs these days. Amazon seems outrageous price wise, but I'm striking out elsewhere. Curious if anyone has better options. TIA

Hi,

I wanted to share something I discovered while testing the 0.18 beta 3 image, in case anyone else is experiencing unusually high CPU usage and can't figure out why.

After quite a bit of troubleshooting, I found that the issue in my setup came down to one setting: the FFmpeg path.

In 0.18, the FFmpeg path is set to default, which uses FFmpeg 8.0. My understanding is that 0.17 used FFmpeg 7.0.

I changed the setting from default to 7.0, restarted Frigate, and my CPU usage immediately went back down to the green range, even under heavy detection.

I knew something wasn't right because I was seeing around 80% CPU usage consistently when normally I hover around 20%.

Glad I managed to track it down, and I figured I'd share in case it helps anyone else running into the same issue.

https://preview.redd.it/268k9m0sr7kh1.png?width=1374&format=png&auto=webp&s=f922bd24fd8e082ae8777eea2b4f277197c48896

Edit:

I solved the CPU usage issue. My fault for not reading the release notes. The fix below also brought my live view events thumbnail functionality back.

Under go2rtc in my config.yaml I had to explicitly set

ffmpeg:
  global: "-vaapi_device /dev/dri/renderD128"

I've done lots of searching on getting yolo9 working and am stuck at the build stage. I just subscribed to frigate+ and want to get the most out of my system. It looks like its failing to connect to the debian servers. Is there something obvious I'm missing or did some links break in the script? Terminal command and output pasted below.

server@server:~$ sudo docker build . --build-arg MODEL_SIZE=s --build-arg IMG_SIZE=320 --output . -f- <<'EOF' FROM python:3.11 AS build RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/* COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/ WORKDIR /yolov9 ADD https://github.com/WongKinYiu/yolov9.git . RUN uv pip install --system -r requirements.txt RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript ARG MODEL_SIZE ARG IMG_SIZE ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx FROM scratch ARG MODEL_SIZE ARG IMG_SIZE COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_EOFE}.onnx [+] Building 734.6s (9/18) docker:default => [internal] load build definition from Dockerfile 0.0s => => transferring dockerfile: 986B 0.0s => [internal] load metadata for ghcr.io/astral-sh/uv:0.10.4 13.4s => [internal] load metadata for docker.io/library/python:3.11 0.8s => [internal] load .dockerignore 0.0s => => transferring context: 2B 0.0s => CACHED [build 1/10] FROM docker.io/library/python:3.11@sha256:7bd2bb 0.0s => CACHED [build 8/10] ADD https://github.com/WongKinYiu/yolov9/release 0.9s => CACHED [build 5/10] ADD https://github.com/WongKinYiu/yolov9.git . 2.5s => CACHED FROM ghcr.io/astral-sh/uv:0.10.4@sha256:4cac394b6b72846f8a85a7 0.0s => ERROR [build 2/10] RUN apt-get update && apt-get install --no-inst 721.0s

> [build 2/10] RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*: 60.26 Ign:1 http://deb.debian.org/debian trixie InRelease 120.3 Ign:2 http://deb.debian.org/debian trixie-updates InRelease 180.4 Ign:3 http://deb.debian.org/debian-security trixie-security InRelease 240.4 Ign:1 http://deb.debian.org/debian trixie InRelease 300.5 Ign:2 http://deb.debian.org/debian trixie-updates InRelease 360.6 Ign:3 http://deb.debian.org/debian-security trixie-security InRelease 420.6 Ign:1 http://deb.debian.org/debian trixie InRelease 480.7 Ign:2 http://deb.debian.org/debian trixie-updates InRelease 540.7 Ign:3 http://deb.debian.org/debian-security trixie-security InRelease 600.8 Err:1 http://deb.debian.org/debian trixie InRelease 600.8 Temporary failure resolving 'deb.debian.org' 660.9 Err:2 http://deb.debian.org/debian trixie-updates InRelease 660.9 Temporary failure resolving 'deb.debian.org' 720.9 Err:3 http://deb.debian.org/debian-security trixie-security InRelease 720.9 Temporary failure resolving 'deb.debian.org' 720.9 Reading package lists... 721.0 W: Failed to fetch http://deb.debian.org/debian/dists/trixie/InRelease Temporary failure resolving 'deb.debian.org' 721.0 W: Failed to fetch http://deb.debian.org/debian/dists/trixie-updates/InRelease Temporary failure resolving 'deb.debian.org' 721.0 W: Failed to fetch http://deb.debian.org/debian-security/dists/trixie-security/InRelease Temporary failure resolving 'deb.debian.org' 721.0 W: Some index files failed to download. They have been ignored, or old ones used instead. 721.0 Reading package lists... 721.0 Building dependency tree... 721.0 Reading state information... 721.0 E: Unable to locate package cmake 721.0 E: Unable to locate package libgl1

Dockerfile:2

1 | FROM python:3.11 AS build 2 | >>> RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/* 3 | COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/ 4 | WORKDIR /yolov9

ERROR: failed to build: failed to solve: process "/bin/sh -c apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*" did not complete successfully: exit code: 100

Hi everyone,

I recently switched from 0.17 to 0.18beta3, and I have to say I am thoroughly impressed with how much of an upgrade this is. The settings section alone is a huge improvement, and I can see it making things much easier for both new and longtime users without having to constantly dive into the config file.

That said, I have noticed a couple of things since upgrading and was curious if other 0.18beta3 users are seeing the same.

The event thumbnails above my live view feeds dashboard aren't populating. Is this a design change in 0.18, or could it be a bug?

I've also noticed noticeably higher overall CPU usage compared to 0.17.

I'm definitely not complaining, especially since this is still a beta. I'm mainly curious whether these are known changes, something isolated to my setup, or perhaps something that can be addressed by tuning the new settings.

I haven't changed any settings since upgrading because I haven't had much time to go through everything yet. Are there any settings in 0.18beta3 that users coming from 0.17 should specifically review or tune?

Just curious if anyone else has noticed the same behavior.

Hello...my coral had a hard day with my dogs chewing...and now is lets say it nicely...kaputt.

Since budget is always low...new is not an option...thx

I was having a lot of issues getting a Coral TPU going in the Raspberry Pi 5 with the latest kernel so got hold of a HailoL8 to try.

Still a few issues getting a working driver going (latest Hailo driver is 4.23 but the docker image wants 4.21). I also had some error messages from the kernel with memory lock errors but found a post on the Hailo forum about modifying the source files and that fixed it)

CPU usage has dropped significantly as have inference times so it seems to be doing it's job.

Lacking any hardware H264 decoding, CPU usage is still a bit on the high side (4 cameras) but not causing any overheating. (checking out affordable, alternative GPU options)

Next issue to tackle is a "Live view is in low-bandwidth mode due to buffering or stream errors." issue. Not sure if this is a network issue or a bad config on my part

Long-time Home Assistant user finally migrating my camera setup off Synology Surveillance Station to Frigate. Current NVR host is a Dell OptiPlex 3050 (i3-7100T) that's well past its limits, so this is a full hardware refresh. Goal was a box I don't have to revisit for several years, with room to add services later instead of being boxed in like I am now.

Would appreciate a gut check before I start ordering and building.

Hardware

Host: MINISFORUM MS-02 Ultra (MS-02U-285HX) (Amazon purchase)

• Intel Core Ultra 9 285HX (24C/24T)
• 32GB DDR5 (single module as shipped)
• 1TB NVMe included
• 4× M.2 PCIe 4.0 slots, PCIe 5.0 x16 slot
• Dual 25GbE SFP28 + 10GbE + 2.5GbE
• Intel vPro / AMT for out-of-band KVM
• 350W internal PSU, 4.8L chassis

Detector/decoder: Sparkle Intel Arc A310 ECO (SA310C-4G) (Ebay purchase)

• 4GB GDDR6, 50W, single-slot low profile, no aux power

Storage: Samsung 990 PRO 2TB NVMe (Ebay purchase)

• Frigate config, database, snapshots

Recordings: Synology DS1522+ over NFS

Cameras

Six Reolink, mixed models:

• Video Doorbell
• Elite Floodlight
• 2× Duo 2
• E1 Pro PTZ
• RLC-410W

All currently recording to Surveillance Station. Plan on two more after everything settles.

Architecture

Proxmox VE 9.x on bare metal.

• Frigate: dedicated VM (Debian/Ubuntu), Docker, Arc A310 passed through via VFIO
• Home Assistant: HA OS VM, migrated from the OptiPlex
• go2rtc: restream layer so both Frigate and Surveillance Station can pull from each camera without hitting Reolink's concurrent RTSP client limits

Deliberate choices worth flagging:

Frigate standalone, not an HA add-on. HA restarts and Supervisor churn shouldn't touch the NVR.

Discrete GPU instead of the iGPU. The 285HX iGPU stays with Proxmox for console, and the Arc gets bound to VFIO for the Frigate VM alone. Avoids the headless i915-blacklist gamble entirely, and vPro is there as a backstop.

NFS mounted inside the Frigate VM, not passed through from the host. Recordings land at a fixed local path, so swapping the NAS for local disks later is an fstab change and nothing else. Database and cache stay on local NVMe — no SQLite over NFS.

tmpfs for /tmp/cache so in-progress segments never touch flash.

Open questions

1. Memory. It ships as a single 32GB stick, so I'm single-channel out of the box. Plan is to run as-is, watch memory pressure, then drop in a matched 2×32GB kit (keeping two slots free) rather than pairing something with the original module. ECC is supported on this model — is it worth doing the all-ECC swap for a 24/7 NVR host, or overkill at home?
2. A310 with 4GB. Fine for detection on six cameras. How does it hold up once semantic search / face recognition / LPR are enabled? Anyone running enrichments on 4GB?
3. The included 25GbE card. I have no use for it (2.5GbE to the NAS is more than enough at my bitrates) and it reportedly runs hot and needs dedicated airflow. Leaning toward pulling it for thermals and a free slot. Any reason to keep it installed?
4. Arrow Lake passthrough. Aware Meteor/Arrow/Lunar Lake need QEMU 10.1+, hence PVE 9. Anyone passing a discrete Arc card through on an HX-series board — any gotchas beyond the usual q35/OVMF/no-ballooning?
5. Am I overbuilt? Six cameras is not a lot. I went this direction specifically for expansion room, but if there's a strong argument I'm paying for headroom I'll never touch, I'd like to hear it.

Any advise / guidance would be great appreciated.

Been fucking around with Frigate for a while now and I’m at the point where it’s working well enough that I want to build the setup out properly. Looking for feedback from people running bigger Frigate installs, especially Hailo + face recognition/LPR.

Current hardware:

• i7-8700K
• Gigabyte Z370 AORUS Gaming 7
• 32GB DDR4
• EVGA GTX 1060 SC 6GB
• Intel UHD630 iGPU
• Proxmox
• Frigate running in an LXC/Docker
• M.2 SSD
• planning to put a 3TB NVR/surveillance HDD back in for event recordings

Currently testing 4 Reolink cameras.

Frigate is running YOLO11m 640x640 through ONNX/CUDA on the GTX 1060. Intel iGPU handles VAAPI decode/scaling.

I started playing with Frigate face recognition and holy shit, I’m impressed. That’s basically what sent me down this rabbit hole.

I found that using the 640x360 substreams for detect was pretty disappointing for faces. I moved my Main Door camera to a 2560x1440 working/detect frame at 5fps and face recognition got noticeably better.

I then got stupid and tried all 4 cameras high-res. 😂

That was too much. Frigate started skipping about 14fps overall.

Current sweet spot is:

• Main Door: 2560x1440 @ 5fps
• Front Doors: 2560x1440 @ 5fps
• Street: 640x360 @ 5fps
• Side House: 640x360 @ 5fps

With that setup I’m getting basically 20.2 process fps / 20.3 camera fps and 0 skipped fps.

GTX 1060 inference is around 36-37ms with YOLO11m. GPU utilization varies depending on activity, but I’ve seen the 1060 hit its 120W power limit when shit gets busy.

The UHD630 was only around 20ish% in my last test, and overall CPU usage was only around 12%, so the 8700K itself definitely doesn’t appear to be my bottleneck yet.

Eventually I want around 12 cameras.

The end goal is:

• person detection
• dogs/animals
• Frigate+ eventually
• face recognition on cameras where people are close enough
• ideally 2K working frames for the important face cameras
• 2K or possibly 4K for dedicated license plate cameras
• good detection quality, not just insane FPS numbers
• low enough power consumption that I don’t feel like I’m heating my house with an NVR

Current server seems to be pulling roughly 150-200W depending on what it’s doing, so power efficiency is becoming a concern.

My current thought is:

Intel iGPU = decode/scale
Hailo-8 = YOLO/object detection
GTX 1060 = keep installed for face/LPR/other CUDA shit if Frigate can actually make useful use of it
CPU = everything else

The big question I have is Hailo detection quality.

I actually really like the quality I’m getting from YOLO11m on the GTX 1060. I don’t want to buy a Hailo and find out I have to drop down to some tiny model that misses shit just to get 100+ FPS.

I understand the GPU itself isn’t what determines detection quality — the model does — and Hailo models are generally quantized/compiled differently. But for people who have actually made the switch:

Can Hailo-8 realistically give me YOLO11m-ish detection quality while handling a 12-camera setup?

And how are you guys handling faces/LPR?

Do you let Hailo handle object detection and use another accelerator/GPU for enrichments? Is the NVIDIA card even worth keeping installed once Hailo is doing YOLO?

I’m also considering rebuilding around a newer Intel platform eventually for lower power and a newer iGPU/media engine. I have access to DDR5 and have been looking at Core Ultra stuff, but I’m not interested in replacing the 8700K just because it’s old. Right now the CPU has tons of headroom.

So I’m basically trying to figure out:

1. Keep the 8700K/Z370 and add Hailo?
2. Build a newer low-power Intel box + Hailo?
3. Use Hailo for detection and keep the 1060 for enrichments?
4. Skip Hailo and just buy a much newer NVIDIA GPU?
5. What hardware are people actually using for 10-12 cams with high-res face recognition and LPR without pulling stupid amounts of power?

Not looking for a theoretical “a Coral can do 900 cameras if nothing moves” answer lol. I’m interested in real setups with multiple simultaneous detections, faces, cars, animals, LPR, etc.

Appreciate any numbers you guys have — camera count, detect resolution/FPS, Hailo model, inference speed, iGPU/GPU utilization, and actual wall power would be especially useful.

I've been struggling to get my frigate solid with one Lorex IP camera (before I add the other 3).

Running in docker on ugreen DXP2800 NAS with 16GB ram (I just ordered a 32GB stick). It will run fine for a day or so with no errors and UI is fine. Then I check the logs and there's a ton of errors. CPU use is over 50 and ram keeps creeping up.

I just read about running it in a VM could drastically help with utilizing the Intel N100 iGPU for better performance.

Any comments on running frigate in a VM vs docker?

I'm also running HomeAssistant in a VM but haven't linked it yet to frigate.

Thanks.

Edit 1:

Trying this now: https://pastebin.com/Ri0GeXzP

Thx for all the help here

Hello everyone...

I had a lighning issue and my motion detector went south...sinc ei also look at the same area with my dahya cam, i thought i could just put a smart switch in the line and have the cam see motion or not and switch the light on and of...

so far so good, works...jsut to good :-) is also recognices the light turn off as motion...and when i look at the debug motion boxes, even the artifacts are causing it.

so i played with the motion threshold and had no luck, cause at some point the light doesnt come on, but also not for me anymore...

so my next idea was to use detection of humans/pets and so on, but i havent found a way to use it this way yet.

Any ideas or is anyone using such setup already and share some wisedom?

frigate is bare metal and home assistant is bare metal.
Here the automation: https://pastebin.com/qjqtfKxy

Here the frigate one: https://pastebin.com/cmERB43h

Driveway cam is the beast i want to use

I handle IT/infrastructure for a group of companies in construction and electrical contracting. We’ve got multiple sites to cover: shops, warehouses, equipment/fleet yards, and a fair amount of open perimeter. Cold climate, so gear takes a beating in winter.
First round was Reolink and it didn’t hold up — random dropouts, cameras going unresponsive and needing power cycles, a few outright failures. Manageable with two or three cameras, not when you’re running a couple dozen across sites and someone has to drive out to reboot one.
What I need:
• Actual long-term reliability — I can’t be replacing units every year
• RTSP/ONVIF, no cloud lock-in (running Frigate, PoE, cameras on their own VLAN)
• Solid low-light for unlit yards, and housings that survive -30°C
• Varifocal options for longer runs across a yard
• Priced sanely — not Axis-for-every-camera money, but I’ve learned the cheap route costs more in truck rolls
What’s the sweet spot for commercial/industrial deployments? Dahua/Hikvision OEMs (Empire Tech, etc.), Amcrest, Vivotek, Uniview, something else? Curious what people are running at this scale that’s still solid after a few years.
Thanks!

I bought a bunch of these cheap ass amcrest ASH21-B's from amazon, to use with frigate inside, set them up in the dumb amcrest smart home app, but didnt want to keep going to the app to adjust Pan/Tilt and Frigate didnt seem to support the DVRIP protocol.

So i went to Codex and verified/tested with Claude to create a proxy for the DVRIP controls. It made a native app and published to Unraid or you can host on your own docker via the instructions https://github.com/gaming09/amcrest-ptz-bridge

This is 100% local verified by packet capture.

This is vibe coded, i had a problem that i needed solved, it solved it, I'm sharing it. if you hate vibe coding don't use it, i don't need to know how you hate vibe coding. If you find a problem with it I'm happy to address it.

Here is some AI slop for how it works and why its needed

I wanted to control my Amcrest SmartHome PTZ camera entirely through Frigate instead of using the Amcrest SmartHome app.

Frigate’s PTZ interface uses ONVIF. Some Amcrest cameras provide a working native ONVIF service, but support can vary by model and firmware. Before installing anything, try pointing Frigate directly at the camera’s ONVIF service. The ASH21 is currently listed by Frigate as supporting basic PTZ on port 80:

cameras:
  patio_camera:
    # Existing ffmpeg/detect configuration remains here.

    onvif:
      host: CAMERA_IP
      port: 80
      user: admin
      password: CAMERA_PASSWORD

If native ONVIF doesn’t work, I made Amcrest PTZ Bridge, which is now available through Unraid Community Applications.

The bridge:

• receives ONVIF PTZ commands from Frigate;
• translates them into local Amcrest/Dahua DVRIP commands;
• handles pan, tilt and stop;
• includes a fail-safe movement timer;
• leaves video/audio streaming directly between the camera and Frigate;
• has no telemetry or cloud integration;
• supports a protected password file instead of a Docker password variable.

After installing it, Frigate points to the Unraid server (or docker IP) instead of directly to the camera:

cameras:
  patio_camera:
    onvif:
      host: UNRAID_SERVER_IP
      port: 18880
      user: ""
      password: ""

Use one bridge instance and a unique port for each camera.

GitHub: https://github.com/gaming09/amcrest-ptz-bridge

In Unraid Apps, search for Amcrest PTZ Bridge.

For the PWNTOOLS usage is as follows

Pwntools is used indirectly by the pinned DahuaConsole dependency—not for pentesting anyone.

DahuaConsole uses it for:

• Opening the TCP connection to the camera with remote(...)
• Sending and receiving DVRIP packets
• Packing binary protocol fields with helpers such as p32()
• Handling socket timeouts and connection errors

the bridge.py only calls DahuaFunctions; it does not directly use pwntools. The camera credentials are passed through that local DVRIP connection to the configured camera on port 37777.

Pwntools normally has an optional version check, which is why we added pwn.conf to disable it. Removing pwntools entirely would require replacing or modifying DahuaConsole’s networking layer and could break camera communication.

https://preview.redd.it/9bufohz6pdjh1.png?width=664&format=png&auto=webp&s=49f5a831c7589b89e788d10f54d5d4c8b0c38358

https://preview.redd.it/krnagphfpdjh1.png?width=1118&format=png&auto=webp&s=a176be9076fab14ff2d3210d2a24aa26b76c52e1

https://preview.redd.it/wvsg4apcqdjh1.png?width=519&format=png&auto=webp&s=2b75a3b0d15a572da6e14c29040effbff1a44d78

Two weeks ago I posted here that I wanted to build a companion for Frigate. It's nearly done now, just short of a 1.0, and I'd be happy about some feedback.

What's in: four ways it recognizes people.

1. After the event, from the full recording. Slowest but most thorough, this one gives the final verdict.
2. By the person itself: build, outline, clothing. Works when there's no usable face at all.
3. Through a vision model. You choose whether that runs locally or against a cloud endpoint.
4. And a live watcher: it spots a face on the camera stream itself and then names the person as fast as it can, so an MQTT message can go out within about a second.

For learning there's no photo uploading. You can run through all your past events and it tells you who was on your property and when, groups the recurring people, picks the faces worth learning by itself, and you can export them too.

Have a look if that sounds useful. I'd really appreciate feedback, good or bad, via GitHub or mail: suslik_dev@posteo.de. (The rocm and gpu-legacy variants I can't test myself, I don't even have the hardware, so reports there help twice.)

github.com/BennoBaer-dev/suslik

https://preview.redd.it/owe08ucds6jh1.png?width=1402&format=png&auto=webp&s=a7fd0cb447d8480e8db461d4b35fcd5305d4cb69

Good morning!

I installed the 'newish' G400 from Aqara about a month ago and after setting it all up, the video stream drops with "No frames have been received, check error logs". It happens at random times, for random lengths.

My frigate has 5 cameras including the G400 and the G400 is the ONLY one that ever drops like this. Also, restarting frigate doesn't help but restarting the camera DOES help. So when Aqara blamed frigate, I am suspicious. But I dont really understand what they're saying.

"Based on the RTSP stream disconnection logs, the issue was not caused by the Aqara camera itself; instead, the disconnection was actively initiated by the RTSP client you are using. For instance, the events associated with log IDs 1786577428 and 1786580048 both involved an active "teardown" operation initiated by the RTSP client."

Is there any way to check in frigate logs to see whether frigate is actually doing? Is frigate really to blame here?

I'm currently using the blueprint from SgtBatten. They work, but are pretty vanilla. I'd like to use LLMs to get richer notifications. Anyone doing anything cool with genai?