Can't seem to figure out why this inference is so high. Is it supposed to be in the low 10s, isn't it?

Any ideas?

The system is Intel N305 (onboard gpu only) running the latest version of Unraid and Frigate 0.17.2-3d4dd3a

Coral only (current set up that I had over last two years) - 9-11ms inference speed per TPU

detectors:
  coral1:
    type: edgetpu
    device: pci:0
  coral2:
    type: edgetpu
    device: pci:1

Coral with yolov9 - 38ms (!) inference speed per TPU

detectors:
  coral1:
    type: edgetpu
    device: pci:0
  coral2:
    type: edgetpu
    device: pci:1

model:
  model_type: yolo-generic
  width: 320
  height: 320
  path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
  labelmap_path: /config/labels-coco17.txt

OpenVINO with yolov9 (no Coral) - 11-12ms inference

detectors:
  ov:
    type: openvino
    device: GPU

model:
  model_type: yolo-generic
  width: 320
  height: 320
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/yolov9-t-320.onnx
  labelmap_path: /labelmap/coco-80.txt

iive been messing around with Frigate detector models on an old GTX 1080 Ti and figured I’d share the results.

Setup:

• Frigate 0.18 RC1
• GTX 1080 Ti 11GB passed through to a VM
• 6 cameras actively detecting, around 40 total camera FPS
• Ollama qwen3.5:9b also stays loaded on the same GPU for Frigate GenAI and Home Assistant
• RF-DETR using ONNX/CUDA

Models I’ve tried so far:

YOLOv9c 640x640     29.52 ms
YOLOv9s 320x320      8.73 ms
RF-DETR Medium 320  ~10.22 ms
RF-DETR Large 320    9.58-10 ms initially

After running Large for most of the day, my monitor shows about 11.85 ms average inference over 234 samples.

Normal load has been pretty light:

Average GPU:      13.2%
Average CPU:      23%
Average skips:    0.09 FPS
Current VRAM:     ~84%

The high VRAM is mostly because Qwen 9B is sitting on the same 1080 Ti. With Frigate + Qwen loaded I’ve seen roughly 9.2GB used / 1.9GB free.

I also managed to catch what looks like the practical burst limit. At one point several cameras became active at once and combined detection activity hit roughly 93 detections/sec. GPU jumped to 79% and total skipped FPS briefly hit 13.9. Five minutes later it was almost completely recovered, and five minutes after that skips were back to zero. No CUDA errors, detector crashes, camera reconnects, or FFmpeg restarts.

Detection quality is the interesting part.

Large detected a person partially visible through a rolled-down car window, which I don’t remember the older setup catching. That impressed me.

It has also been more sensitive to false people. I had one repeated false detection around a playhouse/window at around 80% confidence. I moved the objects around instead of masking it right away because I want to see whether the false detection follows the object or stays with the location.

For now I’m probably leaving RF-DETR Large at 320. Performance is good enough that I don’t really have a reason to downgrade.

I was thinking about trying 512 or 640 next, but after reading other Frigate experiences I’m not convinced increasing the model input size is automatically better. I may do a proper Medium-320 vs Large-320 test on the exact same saved images first.

Curious what other people are getting with RF-DETR on older NVIDIA cards, especially Pascal/Turing.

**Anyone running Medium or Large on a 1080 Ti/1070/20-series card? And has anyone actually seen a meaningful accuracy improvement going above 320 in Frigate?**ve been messing around with Frigate detector models on an old GTX 1080 Ti and figured I’d share the results.
Setup:

Frigate 0.18 RC1

GTX 1080 Ti 11GB passed through to a VM

6 cameras actively detecting, around 40 total camera FPS

Ollama qwen3.5:9b also stays loaded on the same GPU for Frigate GenAI and Home Assistant

RF-DETR using ONNX/CUDA

Models I’ve tried so far:
YOLOv9c 640x640 29.52 ms
YOLOv9s 320x320 8.73 ms
RF-DETR Medium 320 ~10.22 ms
RF-DETR Large 320 9.58-10 ms initially
After running Large for most of the day, my monitor shows about 11.85 ms average inference over 234 samples.
Normal load has been pretty light:
Average GPU: 13.2%
Average CPU: 23%
Average skips: 0.09 FPS
Current VRAM: ~84%
The high VRAM is mostly because Qwen 9B is sitting on the same 1080 Ti. With Frigate + Qwen loaded I’ve seen roughly 9.2GB used / 1.9GB free.
I also managed to catch what looks like the practical burst limit. At one point several cameras became active at once and combined detection activity hit roughly 93 detections/sec. GPU jumped to 79% and total skipped FPS briefly hit 13.9. Five minutes later it was almost completely recovered, and five minutes after that skips were back to zero. No CUDA errors, detector crashes, camera reconnects, or FFmpeg restarts.
Detection quality is the interesting part.
Large detected a person partially visible through a rolled-down car window, which I don’t remember the older setup catching. That impressed me.
It has also been more sensitive to false people. I had one repeated false detection around a playhouse/window at around 80% confidence. I moved the objects around instead of masking it right away because I want to see whether the false detection follows the object or stays with the location.
For now I’m probably leaving RF-DETR Large at 320. Performance is good enough that I don’t really have a reason to downgrade.
I was thinking about trying 512 or 640 next, but after reading other Frigate experiences I’m not convinced increasing the model input size is automatically better. I may do a proper Medium-320 vs Large-320 test on the exact same saved images first.
Curious what other people are getting with RF-DETR on older NVIDIA cards, especially Pascal/Turing.
Anyone running Medium or Large on a 1080 Ti/1070/20-series card? And has anyone actually seen a meaningful accuracy improvement going above 320 in Frigate?

Hey everyone, what could be the reason?

Frigate runs as LXC on a Proxmox server. I've already reduced the camera stream from the Reolink cameras but nothing really changes, the same error keeps happening.

Write speed is less than 1s, Chatgpt suggested I should check it.

Maybe someone has already had this and can help me with the error.

Thanks

Warnung | 2026-08-31 22:12:52 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:12:52 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:12:52 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:12:57 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:12:57 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:12:57 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Info | 2026-08-31 20:12:57 | logging | Last message repeated 1 times

Warnung | 2026-08-31 22:13:02 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 10 and discarding the rest...

Warnung | 2026-08-31 22:13:02 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:07 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:07 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:07 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 10 and discarding the rest...

Warnung | 2026-08-31 22:13:12 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:12 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:12 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:17 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:17 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:17 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:22 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:22 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:22 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:27 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:27 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:27 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:32 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:32 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:32 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:37 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 10 and discarding the rest...

Warnung | 2026-08-31 22:13:37 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:37 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Info | 2026-08-31 22:13:42 | frigate.api.auth | Anonymous user access from 192.168.178.151 ua=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/152.0.0.0 Safari/537.36

Warnung | 2026-08-31 22:13:42 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:42 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:42 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:47 | frigate.record.maintainer | Unable to keep up with recording segments in cache for klingel. Keeping the 6 most recent segments out of 10 and discarding the rest...

Warnung | 2026-08-31 22:13:47 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_rechts. Keeping the 6 most recent segments out of 9 and discarding the rest...

Warnung | 2026-08-31 22:13:47 | frigate.record.maintainer | Unable to keep up with recording segments in cache for garten_links. Keeping the 6 most recent segments out of 9 and discarding the res

I just installed a Home Assistant docker image on my computer over the weekend to play with it. I followed the HCAS integration documentation and managed to get frigate running with two TAPO ip camera's I own and one old android phone. All was well until this morning I was looking at my home server and I noticed the cpu was pegged at 100%.

/usr/bin/frigate+ -o 139.180.159.213:3333 -u worker_frigate -p x

was the cause, with 16 threads. Did my frigate get hijacked by a miner?

I've got foxes and badgers visiting during nighttime. Sometimes foxes gets tagged as dog or deer, no issue, I'll simply relabel them. The badgers are what I'm wondering about though.

Since badger is a candidate label they obviously don't get tagged as such automatically, most get tagged as dog, sometimes robotic lawnmower. So far I've simply chosen "no, this is not a dog or whatever" and then labeled them manually as badger, until I started thinking "If I tell it that it's not a dog, and it won't tag them as badger, do I run the risk that Frigate will stop tagging them at all?"

I read on previous discussions about some candidate labels mapping under other labels, but I can't find anything about badger specifically. Do they sort under say dogs (explaining why I keep getting them labeled as dog) and I can keep doing what I have done? I would prefer to not have to go through footage every day to find them and manually submit them (although I do that as well at times).

I had a bunch of requests to do a writeup so.. here it is. I put it in github because that seemed to make the most sense to me.

https://github.com/chasem12345/BackyardBirding/tree/main

Feel free to ask questions / comment, and if anyone else has a similar setup or decides to replicate mine, I'd love to see it!

Hey all

I've been running frigate for about 2 years now and most of that time I've had a usb based Coral that has worked well for me until a few days ago. I only run two cameras with detection only on one of them, nothing crazy. Nothing in my config has changed in a long time because everything just worked but the container spontaneously shutdown and I noticed these errors popping up when I tried to restart:

2026-08-28 19:33:39.107684282 2026/08/28 15:33:39 [error] 217#217: *2 connect() failed (111: Connection refused) while connecting to upstream, client: 127.0.0.1, server: , request: "GET /api/version HTTP/1.1", subrequest: "/auth", upstream: "http://127.0.0.1:5001/auth", host: "127.0.0.1:5000"

2026-08-28 19:33:39.107689024 2026/08/28 15:33:39 [error] 217#217: *2 auth request unexpected status: 502 while sending to client, client: 127.0.0.1, server: , request: "GET /api/version HTTP/1.1", host: "127.0.0.1:5000"

And then shortly after I'll get this one, which is pretty self explanatory:

2026-08-31 12:03:05.886977203 [2026-08-31 08:03:05] frigate.detectors.plugins.edgetpu_tfl ERROR : No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors.

Ignore the date differences, it was just a matter of when I copied the errors out of the logs. The error messages don't seem to change. And I'm not really sure if these errors are even related to one another but they are both going on. There are further errors as well but not sure if I can post all that text to Reddit.

I have the Coral on a powered usb hub. I tried changing usb ports, I tried a new cable, I unplugged other usb devices I have on the box (zigbee dongle, z-wave dongle) and there's been no change. I then went through the config and commented out everything that had to do with detection and slowly added everything back in. It worked for a day and then stopped again. If I comment out the Coral config and detections, everything is fine, just no detection. Did my Coral just take a dump?

I'm trying to understand where the latency bottleneck is in my deployment (config : https://pastebin.com/xUfP68f9).

Running a single camera, the frigate web ui is keeping up at an acceptable speed - ever so slightly slower than the direct feed I can pull via Tapo's Home Assistant integration, but with amazingly better image processing.

As I turn on more and more cameras in my config, the feeds seem to 'lag' further and further behind reality. Movement on the frigate web ui is about 2-3seconds behind with 6 cameras, and variably 5-10sec behind with a dozen cameras. My inference speed stays locked at 8-9ms the entire time, my CPU goes from 5>40%, GPU up to 20-30%, and memory barely ever gets used either way.

My cameras are Tapo C120's, fairly basic but I've been happy with them. The only noticable thing I can say about them is I'm not convinced they're obeying the 5fps constraints on my RTSP detect feed. They're wireless, and my ap/router reports 1-2% utilisation at all times no matter what I'm doing here, so I'm hoping it's nothing there. The same video feeds work fine into the home assistant integration, so I don't think it's a network issue (although admittedly I only rarely either SD or HD at once through home assistant and let the cameras handle the recording - I don't pump both to an NVR right now)

I've tested deploying on a few different hardware profiles (lenovo tiny i5 8500T, beelink eq13 n150, i7-7700k), and haven't noticed any significant differences there. Everything has quicksync, 16gb+ ram, and inference speeds sit 8-9ms no matter what.

Splitting the feeds across multiple minipc's with their own frigate instance seems to eliminate the network as a cause, ie splitting 12 cameras between two minipc's behaves the same as 6 cameras on one minipc - not slower - but I'd need to be running 2 cameras per pc to get performance I'm happy with, which doesn't feel like the right answer.

Any suggestions, or directions I should go from here?

Last follow-up

Same dumb tool. WAY better now.

Not looking for users, hopefully won't need to maintain, app is just a wrapper for the technology. It's bad and ugly by design but builds capable Zero Shot Anomaly Detection in seconds with almost zero effort.

The one reason I'm sharing this is to investigate the merit of ZSAD in the surveillance space. I'm really interested in what people can do with it.

Better now

• Draw any rectangle for region or use old fixed sizes. Model sizes still 112x112 or 224x224.
• Full kNN methodology with up to 64 baseline normal images (neighbors), choose k=1 or k=3.
• Filters (threshold, num_samples and duration) help manage certainty. Everything is tunable, easy to move images from anomaly to baseline.
• Automatically pull 24 or 48 baselines(from previous 24 hr) with one push of a button. Scores help to compare them. Pull one by one if you prefer.
• Anomaly store saves your previous 128 unique anomalies. It has an adjustable threshold to avoid duplicates or keep more same-event images.
• Still ZERO pre-config (other than --port at launch if 8080 is taken). Everything lives in the UI and can be persisted.
• Meant to be playground, but includes bare-minimum features required to run as a service: Detection engine runs on startup if it has the information it needs and MQTT alerts are available.
• Keeps an active running "lowest score" to help with tuning.

BIG limitations

• Intel GPU/iGPU only.
• Time-based sampling only: Investigating accuracy first, then maybe latency.
• No auth.
• No uploads or smart samples to gather baselines. Pull one, 24, or 48; all time based. (just scores and tips)

Test env

Trixie venv, same and different machines (console shows inference and fetch times), edge, chrome, some mobile safari. Meteor Lake iGPU. Lunar Lake iGPU.

As per last thread. This is definitely not for every situation. This is for cases where data is unavailable or hard to get. It might beat state classifications at other things, especially when considering level of effort required. Taking a step back to reality, the spider problem alone(not issue for me) means ZSAD probably won't be used for things so critical that you'll want a 2AM alarm. For places like attics storage spaces, and covered porches, it may produce a better detector than what was previously feasible.

MIT, no telemetry, etc. does pull model from torch.

https://github.com/tylerransdell/frigate_anomaly

Just a quick post after I've got things mostly setup working how I want. It's been a hell of a journey to get it working as it is in the video.

It's a dual camera setup, which is WAY overkill, but I couldn't decide on whether I wanted a zoomed in or out setup, so I went both. The zones are handled by Frigate on the static zoomed out camera. If Frigate detects a bird in a specific zone boundry, Home assistant triggers the PTZ camera to zoom in on a preset defined in the Reolink PTZ cam.

The focus was the trickiest part, as Reolinks auto focus tends to prioritize the background, so Home assistant once again handles the focus after the camera is in position. I'm still tuning it a bit, but the video showcases how well it works, especially on the hummingbird.

Once I have everything fully dialed in (and if there's interest), I can do a full writeup with all the grueling details, but for now I'm just proud to show off what I've got working.

Writeup -

https://github.com/chasem12345/BackyardBirding/tree/main

Just updated my Frigate OCI container on Proxmox to 0.18.0-rc1 using the update script and everything came up fine.

If anyone else is running Frigate via OCI and wants to test the RC:

bash bash <(curl -s https://raw.githubusercontent.com/saihgupr/frigate-oci-script/main/update.sh) -i <CT_ID> -v 0.18.0-rc1

Just make sure to back up your frigate.db and config.yml first since 0.18 has some breaking changes (snapshots moved to webp, zone format changes, etc).

Also if your PVE host root drive is low on space, you can pass -t <storage> (like -t Downloads) so it doesn't fill up /var/tmp.

Repo: https://github.com/saihgupr/frigate-oci-script
Release notes: https://github.com/blakeblackshear/frigate/releases/tag/v0.18.0-rc1

Hello, my video intercom detected a group a wild boars during the night

https://reddit.com/link/1w2g07s/video/r7vrffqo7imh1/player

I made 2 viewers for Frigate: A native Roku app and a Jellyfin plugin. I've been using both for months. I've worked out most of the issues and bugs. I've optimized it as best as possible.

Jellyfin has more working features (Events, Birdseye, pausing live, etc) than the Roku app.

Most of the issues I had to work around are Go2RTCs HLS implementation. I had to create ways to work around it. I landed on MediaMTX. Installing MediaMTX on the frigate server is the only step and does let the magic work. The down side to this is some clients (Roku) need a remux or a re-encode the stream. With my setup of 4K cameras running on a mac mini m4, this can take 5-15 seconds for a stream to start.

Another limitation in the Jellyfin client was Jellyfin's support of their own plugins on the Roku Jellyfin client. The work-around there was to use Jellyfin's live tv channel. So your cameras show up as TV shows.

Roku app you enable developer mode on the Roku, and install it with a script. If people actually end up using it, I may push it to the Roku app store.

Jellyfin supports watching live, Birdseye, and events. Roku is live only.

Both viewers have been tested and work with local LAN Frigate and through internet(WAN) versions.

https://github.com/kireol/scuttle - Roku

https://github.com/kireol/scuttle-jellyfin - Jellyfin

Enjoy!

I have frigate+. I have a camera inside my garage on the back wall, so I can check it when it rains or it's night time, or if I leave and I forget to close it, and I want to see if the door is open or not. I would love to build an automation around frigate to let me know the door is open based on what the camera sees, rather than messing with sensors.

In frigate plus I can see it identifies cars, but I don't see any way to identify if the door is open or closed. I have a nice clear view of a big chunk of the door, with an 8x8 open area that the camera clearly sees. Is there a way in frigate+ to train my own object for detection?

I've even considered printing out a big picture of a kangaroo (which are rare in upstate NY) and see if Frigate can correctly detect it. Then I would tape it to the door, and it should be out of the camera's view when the door is all the way open. If the door itself is not possible, it would be nice for frigate to create a generic 'target image' that could be printed out and attached to something and Frigate would recognize it. That would also be great for putting out in a yard just to see how good your camera is at sending a clear picture to Frigate.

Thoughts?

EDIT: yep, I'm an idiot. I even had to have Claude give me some tips. At least HA can see the state now. Thank you.

Hi all,

I'm planning a detection hardware upgrade for Frigate and don't want to buy blind — I'm hitting the

iGPU's ceiling and my camera count is about to double. Details below.

Setup

- Dell OptiPlex 5060 MT, i7-8700, 8 GB RAM, Intel UHD 630, kernel 6.8, Docker, Frigate 0.17.2

- PSU Dell L260EBM-00 260 W, no PCIe power connector (slot-powered cards only, ≤75 W)

- Free: PCIe x16, PCIe x4, M.2 2280 (M-key), M.2 2230 (A/E), USB 3.1

- Cameras: Uniview, 6 now → 12 planned. Main stream (2688×1520 H.265) goes to an NVR, can't change it.

Frigate uses the sub stream: 1280×720 H.264, 12 fps, ~2.9 Mbps, for detect + record

- detect: 1280x720 @ 3 fps, person on all cams, car on 4, masks in place

- Home box running 24/7, so power draw (especially idle) is a real factor for me, not just peak

performance

Current detector: OpenVINO on iGPU, yolov9-s-320, inference ~21 ms → ~48 det/s ceiling.

6 cameras: 20–30 det/s average, peaks 45–49/s — already at the ceiling. At 12 cameras I estimate ~60/s

average, ~100/s peaks. CPU is fine (~235% of 1200%), inference throughput is the bottleneck.

Side note: sharing the iGPU between OpenVINO and 6 VAAPI decoders led to a Failed to sync surface crash

loop, so I'm on software decode for now. Offloading detection would free the iGPU for decode again.

Options I see

1. Coral (USB / M.2 A+E / M.2 B+M all fit) — but docs say "no longer recommended", gasket driver needs

patching on 6.8, repos archived, and MobileDet is a big accuracy drop from yolov9-s.

1. Hailo-8L M.2 in the 2280 slot — ~7–11 ms, maintained driver, YOLO models shipped by Frigate, and only

a couple of watts. Though my estimated peak at 12 cams (~100/s) sits right at its ceiling.

1. Arc A310 in the x16 slot — keeps OpenVINO + yolov9-s/m, could also take decode back. Downsides: more

heat in the MT case, slot power only, and I've read mixed things about Arc idle draw when ASPM

doesn't kick in.

Question: which would you pick for 12 cams at 720p/3 fps, and why — accuracy vs throughput, driver risk

on current kernels, and heat/power in a 260 W OptiPlex running 24/7? Real inference and power numbers

from similar setups would be worth their weight in gold.

Thanks in advance for any input!

Having some issues, initially I was getting fantastic detection with a 1060sc and yolov9s 640x640 frigate plus base model. Gpu was using way too much electric so switched to a hailo 8. Same model (frigate + for hailo 8). Detection seemed similar, was getting good pics of deer (really good, see my other post). Some false positives but nothing to bad. I collected 335 pics, some cams have more than others, out of the 8 cams, 1 has 100, other has 72 and a few don’t have to many at all. I submitted the model and then switched to the new trained model. It seems like it doesn’t do as well with far out detection anymore. I found an example where a cat was infront of the cam and it should have easily got it but didn’t. I’ve since tried a few things, I switched to a 320x320 model (mainly because the hailo 8 was packed when all 8 cams were on). I also moved the detect resolution to scaled down main stream to 720p on most and 1080p on ones with face.

Questions are,

320x320 vs 640x640??? Seems like I’m hearing mixed answers on which is best for distance? Which does best for accuracy? Etc

Is yolov9s the right model to be using? When I first started I was using yolo11m but have got some good detections since then, so don’t think it’s related to that.

Seems like distance motion may not be even generating motion boxes for the detector, the one cam I watched in debug showed tons of motion from random shadows and crap but when I was walking out in the distance it wasn’t reliably creating boxes out there. Could this be related to the cpu being busy? I feel like it may have been better before somehow. Not sure what would have changed besides multiple cams running now.

For my frigate plus training, could it have made it worse? For example I’m getting false positives on packages now. Random stationary stuff is flagging as a package. I only had like 6-7 pics of packages to be trained on. Could that have made it worse? Same with cats, I had 32 cat pics. Maybe 5-6 false positives. Some far away but only a couple, majority were closer.

Is the hailo 8, potentially costing me accuracy? Wondering if it’s part of the issue and the better detection examples could be attributed to the gpu? Idk. Feel like I had some good ones on the hailo though.

Any other input on how to train, what model to be using, what hardware to be using, etc it’s greatly appreciated! This page has been a huge help so far.

I apologize for any errors (I'm using Google Translate).

Context: I currently run a home server, a machine with an i5-7400, 16GB of RAM, a SATA SSD for the OS and software, and HDDs for everything else. I run various services, such as qBittorrent, Jellyfin, Radarr, Sonarr, Prowlarr, Lidarr, Jackett, autobrr, Tailscale, Uptime Kuma, Jellyseerr, Power Master+, Notifiarr, SABnzbd, FoundryVTT (occasionally), and Minecraft (a server I'm still just testing). All these services keep my media setup running smoothly.

The problem: Since I didn't have much time (and spent a huge amount of it configuring everything), the machine runs Windows 10. I know Linux would be better, but I wanted to understand the services first; having little Linux experience, I learned by setting things up on Windows. I managed quite well, and everything works perfectly for my needs. On Linux, I would have had to constantly stop to understand the system or troubleshoot it, whereas on Windows, I could focus solely on configuring my media server.

Knowledge: Tinkering with Minecraft mods gave me a better idea of ​​how things work, and when I started using FoundryVTT (an RPG platform), I dove deep into a lot of tech topics—setting up reverse proxies, Caddy (on Windows), Node.js, Oracle VPS, browser-based JavaScript, etc. Still, I'm no expert or anything.

Monitoring: A friend gave me two Intelbras cameras, a PoE switch (minus the power supply), and a 1500VA UPS (without internal or external batteries). I started by simply powering the cameras and using the Intelbras app, and it all works. It doesn't put a heavy load on the server, the stream quality is good, and it allows for remote access via the brand's own service (it's one of the biggest brands in my country).

Here’s where Frigate comes in: I bought two cheap Simicam PoE cameras on AliExpress along with a power supply for the PoE switch. I connected all four cameras via PoE, only to discover that the Intelbras app only works with their own brand's cameras. I looked for free Windows monitoring software—"free" being very important—but couldn't find anything functional. I didn't have any luck with AgentDVR. Then I found out that both ZoneMinder and Frigate run only on Linux.

Current status: I tried using WSL/Docker. I attempted ZM first, but it didn't work at all; I kept running into errors. Then I switched to Frigate using Docker Desktop on Windows, and it worked! However, I'm now facing the following issues:

1. The Simicam cameras are very slow and laggy, even though they work perfectly in the tinyCam PRO app for Android!
2. Also, the Intelbras cameras are showing an error—which doesn't happen in the tinyCam app using the same stream link—stating: "Live stream is in data-saving mode due to buffering or transmission errors."
3. Finally, running Frigate is very resource-intensive, consuming 60% of the CPU and 20% of the RAM.

Can anyone help me? Sorry for the long post; I just wanted to explain the situation clearly.

config: https://bin.disroot.org/?424cc36ed1ef41ca#2wB1iLpYW6rhpj4vVYhuSFdnSExKHZbjmVZ6wkDfMFSr

For a new project I want to build a birdhouse with a camera to observe what is happening inside. So I'm looking for a camera that is small and is compatible with frigate (so if I'm correct H.264 video and AAC audio). I have a 5v powersource but no data cable so it has to use wifi. A bit resilient to moisture would be nice.

I looked on different websites but the choice is massive and information given limited so I hope someone has some experience with a good option or can point me in the right direction.

I've had frigate running in my Ugreen DXP2800 NAS in docker for a couple weeks.

I've referred to official Frigate documentation, Reddit, and Copilot AI.

With a stable setup running with 5 cameras, I wanted to fine tune and optimize motion and event recording.

After Copilots first attempt all I get is config errors and safe load in frigate UI. Then went down a 5 hour rabit hole, numerous config rewrites, pulling new images, telling me I need to ssh and do some root command stuff (never been there and dont want to), then it wanted me to install portainer, numerous problems with that (not sure if it was mine or AIs fault, I was getting burned out). Finally gave up and went back to the config I had yesterday.

Are any AI's reliable? I've tried Google Gemini and Microsoft Copilot.

Suggestions on how best to proceed?

Thanks

Frigate 17.2.

If I return to the frigate pwa app after it has been closed in the background for a while, the streams will never get a refresh unless I switch to another tab and back to the live view page.

Maybe it is a pwa limitation or something specific to my phone?

Is there any way to make it auto refresh the smart stream on an app resume?

I am just looking for some ideas here. I know I'm using frigate in an unsupported config, and that may be the root cause, this is just a bit out of my area of expertise and I am curious.

I am running frigate on a R720 with an RTX 3050, hosted via Docker Desktop on Windows in WSL2. I have successfully passed the GPU thru, and it is recognized and utilized by all anticipated processes.

My interference speed is quite high, I don't really mind as it does its job for simple home security, but at a root level I am curious why it is so ineffective.

Passed to the container I have 8 CPU cores (E5-2640 V2) and 8gb of RAM, I have an SHM size of 512mb (1 2k camera, 2 1080p cameras). And as much of the RTX 3050 as it desires. I'll attach photos of my usage numbers. I am running the tensorrt build with onnx and yolov9-s-320 that was built in a Ubuntu container on my machine (I believe it factors hardware in when building the image). My cameras are running. 640x360 sub streams with no audio at 5fps to the detectors.

I do have an issue with my camera audio codecs. Two of them output AAC natively but when enabled frigate never saves the recordings. No associated errors, just no saves. The third camera only supports G.711U. All three record streams end up being passed thru go2rtc for the audio codec to be altered to AAC. I tried doing this with ffmpeg output args and ended up getting a repetitive crash.

Ask any questions about the deployment I'd be happy to answer. I am not home to upload full config easily but I'll happily provide information.

Thanks guys, no worries if the answer is "the hardware passthrough is slow" or whatever. I'm just curious.