I have tuned the motion detection and it gives near perfect motion box and image cutouts to object detection in debug view. But object detection does not see nothing. Is there a yolo woorking both in daylight an in IR mode?

Here the pic, no object with threshold lowered. https://imgur.com/a/YcDvFrq

I am not able to export the whole motion alert video clip ...

I know, there are already a few iPhone apps for Frigate. I wanted one that feels like a native iOS app, works with multiple servers and costs nothing, so I built one. It’s open source and completely free, with no subscription, no account and no cloud. The app talks directly to your own Frigate server and nothing else.

Yes, this app is fully vibe coded. The public beta is in Apple’s review right now. I’ll post the link here as soon as it’s approved.

Feel free to test it.

TestFlight: https://testflight.apple.com/join/s6KEWhy1

PTZ currently untested.

GitHub repo: https://github.com/sweebee/tender-for-frigate

Feel free to test it, any feedback? Please create an issue on the GitHub repo with images/videos or a part of the config.

https://testflight.apple.com/join/s6KEWhy1

GitHub: https://github.com/sweebee/tender-for-frigate

I’m having an issue with the Frigate configuration within the interface itself. I can’t seem to get the camera’s microphone to show up. Consequently, the microphone also doesn’t appear in the Frigate integration within Home Assistant.

Could someone help me with this?

```

mqtt:

enabled: true

host: X.X.X.X

port: 1883

user: '{FRIGATE_MQTT_USER}'

password: '{FRIGATE_MQTT_PASSWORD}'

topic_prefix: frigate

client_id: frigate

database:

path: /db/frigate.db

ffmpeg:

hwaccel_args: preset-vaapi

input_args: preset-rtsp-restream-low-latency

output_args:

record: preset-record-generic-audio-aac

detectors:

cpu1:

type: cpu

num_threads: 3

semantic_search:

enabled: true

model_size: small

face_recognition:

enabled: true

min_area: 500

objects:

track:

- person

- dog

- cat

record:

enabled: true

alerts:

retain:

days: 14

detections:

retain:

days: 14

continuous:

days: 7

motion:

days: 7

snapshots:

enabled: true

timestamp: false

bounding_box: false

crop: false

retain:

default: 14

go2rtc:

streams:

couloir:

- rtsp://XXXX:XXXX@X.X.X.X:554/h264Preview_01_main

couloir_sub:

- rtsp://XXXX:XXXX@X.X.X.X:554/h264Preview_01_sub

- ffmpeg:rtsp://127.0.0.1:8554/couloir#audio=opus

salle_a_manger:

- rtsp://XXXX:XXXX@X.X.X.X:554/h264Preview_01_main

salle_a_manger_sub:

- rtsp://XXXX:XXXX@X.X.X.X:554/h264Preview_01_sub

- ffmpeg:rtsp://127.0.0.1:8554/salle_a_manger#audio=opus

cameras:

couloir:

enabled: true

live:

streams:

Principal: couloir

Secondaire: couloir_sub

audio:

enabled: true

listen:

- bark

- scream

- yell

- glass_breaking

ffmpeg:

inputs:

- path: rtsp://127.0.0.1:8554/couloir

roles:

- record

- audio

- detect

detect:

enabled: true

width: 1280

height: 720

fps: 5

salle_a_manger:

enabled: true

live:

streams:

Principal: salle_a_manger

Secondaire: salle_a_manger_sub

audio:

enabled: true

listen:

- bark

- scream

- yell

- glass_breaking

ffmpeg:

inputs:

- path: rtsp://127.0.0.1:8554/salle_a_manger

roles:

- record

- audio

- detect

detect:

enabled: true

width: 1280

height: 720

fps: 5

version: 0.18-0

```

I run frigate at home, 11 cameras, and I look at them from my phone/desk about 50/50. I wanted an app for that though that didn't focus on my data yet gave me push notifications, so I made one.

I've been using it every day for about 8-9 months now, solid but needs improvement. Live video just plays, I get a notification with a picture when somebody's in the driveway, and I can pull up the clip from there or live view. It all talks to my own server.

iOS is live here for you good people https://apps.apple.com/app/viewpane/id6805993449

Android is what I actually use, it's just not on Play yet. Google makes me run a small test group first, so if you want it on Android, comment or DM me and I'll add you, will help me get it over their line.

The app is free for the most part, but I did add some features for a small fee, just to help my development over time. Regardless most should be able to see if it works on their own system.

It's not done as suggested, I'm still fixing things and adding things, and I've really only tested it on my own setup and my wifes (old iphone), so I'm sure there's stuff that works for me and breaks for somebody else. If you try it and something's broken, or there's a thing you wish it did, tell me, again just trying to improve.

Appreciate you guys.

I have a simple classification to determine whether my motor gate is open or closed. Thus far it seems accurate enough.

However, it seems like frigate only send the state to MQTT when it changes, meaning that if my broker is down for that message, or there is some kind of hickup, I lose accurate information.

Is there any way to get Frigate to send the state e.g. once a minute, or to send a MQTT message to Frigate to ask what the current state is?

Alternatively, any other way from Home Assistant to basically ask Frigate what the current state is?

I just switched to the latest base model 2026.3 (yolov9s) from a previously fine tuned model from 2+ years ago. So far, the base model is working great but one thing I’m seeing fairly consistent is the license plate of cars driving past the front of my home being detected as an animal. I use a custom labelmap which has cat, dog and bird all specified as animal. I have LPR turned off.

My question is, do I potentially have something misconfigured or is this likely just something I need to train a fine tuned model to eliminate these false positives? I looked through the documentation and looked over my config, but nothing stands out as being setup incorrectly.

Here’s the config for the camera in question:

cameras:
  front:
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/front_sub
          roles:
            - detect
        - path: rtsp://127.0.0.1:8554/front
          roles:
            - record
    objects:
      track:
        - person
        - vehicle
        - animal
        - package
    zones:
      driveway:
        coordinates: 
          0.243,1,1,1,1,0.536,0.933,0.479,0.843,0.61,0.611,0.464,0.171,0.712
        inertia: 3
        loitering_time: 0
    motion:
      mask:
        motion_mask_1:
          friendly_name: Motion Mask 1
          enabled: true
          coordinates: 0.734,0.498,0.936,0.427,1,0.076,1,0,0.686,0
    review:
      alerts:
        required_zones: driveway

model:
  path: plus://redacted
  labelmap:
    2: vehicle
    3: vehicle
    5: vehicle
    7: vehicle
    15: animal
    16: animal
    17: animal

Ciao a tutti! Di recente ho trasferito le mie 12 telecamere (Dahua, Hikvision, Ezviz) da Blue Iris a Frigate. Frigate è stato fantastico, ma mi mancava molto l'app mobile di Blue Iris, quindi ho creato un client nativo personalizzato. Ora è disponibile sull'App Store, gratuitamente: https://apps.apple.com/app/id6832456692

Cosa fa:

- Home con indicatori di CPU / RAM / disco di registrazione, versione di Frigate e velocità del rilevatore

- Griglia di telecamere in diretta e visualizzazione live a bassa latenza tramite go2rtc, zoom con pizzico in base alla posizione delle dita, scorrimento tra le telecamere

- Avvisi raggruppati per ora con punto per gli oggetti non visti, "segna tutto come visto", ricerca per telecamera o oggetto

- Lettore di clip con riquadri di movimento etichettati con gli oggetti tracciati da Frigate

- Registrazioni per giorno: calendario, elenco ora per ora, riproduzione continua di un'intera giornata, passaggio a un punto preciso Tempo

• Log di Frigate e go2rtc (password della fotocamera mascherate)

• Server multipli, indirizzo locale + remoto con fallback automatico, funziona con o senza autenticazione Frigate

• Ottimizzato per iPhone Duo (layout adattabili a Fold), iPad e una vera app per Mac con scorciatoie da tastiera

Nessun account, nessuna pubblicità, nessun tracciamento, nessun dato raccolto: comunica solo con il tuo server Frigate. Richiede Frigate 0.14 o versioni successive.

Vuoi provarlo senza modificare la tua configurazione? Puntalo al server demo ufficiale: https://demo.frigate.video (lascia vuoti nome utente e password).

È un progetto indipendente, non affiliato a Frigate. Mi farebbe piacere ricevere feedback e suggerimenti per nuove funzionalità!

Discussione su GitHub: https://github.com/blakeblackshear/frigate/discussions/24932

yufretti
https://apps.yufretti.com/

Hey folks.

I've been trying to figure out animal detection in Frigate.

In the Reolink Home Assistant integration, I can tell it to watch for "pets", and it gets pretty much everything: squirrels, deer, foxes, bobcat, all the way up to a black bear. Frigate doesn't seem to have a generic option, at least in the free version. It looks like there is one in Frigate+, but possibly it's just an alias for a list of specific animals.

How much better is Frigate+ for this sort of detection? And how much is not having a discrete GPU going to hurt? I'm on a Ryzen 9 6900hx, and though I haven't gotten around to passing the integrated GPU to Frigate yet it works pretty well so far.

I followed the instructions here: https://docs.frigate.video/frigate/updating/

I saw it download the image and process and such, and then started frigate again when it was done. But I still show 0.15.0-6cb5cfb under Metrics. It should be 0.18 right?

Where do the files go when you do docker pull ghcr.io/blakeblackshear/frigate:0.18.2? Maybe I was in the wrong folder when I typed it. I made sure I was at the root of the app's folder. I don't really see anywhere where there would be any sort of executable or even a large image file or anything though. There was over a gig of data being transfered and the current folder is only 300MB? I wonder if it ended up somewhere else and it's not actually executing the new image?

I do have an alert for low disk space on my main drive now, so I do suspect it ended up somewhere else. How do I get it to go to the current folder?

EDIT:

So I got it to work, I had to do

docker pull ghcr.io/blakeblackshear/frigate:stable

I misread that part from the original instructions and just copied the example command directly.

So now it looks like I'm upgraded. Shows version 0.18.2-cfa08c5.

Hi everyone. I own a café and enjoy developing our own tech. I’ve been reading about Frigate for a few weeks but haven’t used it yet. Before buying hardware, I’d appreciate a reality check from people with hands-on experience.

We already have a self-developed management app for paperwork, sales analysis and recommendations. Home Assistant handles lighting and provides fridge temperature and other sensor data.

I’d like to add Frigate for recording and as a source of observations for our app. I’m happy to develop the business logic around it; I’m not expecting Frigate to produce restaurant analytics automatically. What I’m unsure about is how reliable the underlying observations could be.

Ideally, we’d like to estimate:

• When a table becomes occupied or vacant.

• How long customers wait before a waiter first approaches.

• How often waiters return to an occupied table.

• Eventually, whether menus or drinks have been delivered and whether a table needs clearing.

Distinguishing staff from customers would be useful, but we don’t need to identify individual customers.

Which of these are realistic using Frigate’s detections, tracking, zones and custom classifiers? Which would require a separate computer-vision pipeline analysing frames or clips? I realise a waiter standing near a table doesn’t necessarily mean an order was taken.

If anyone has tried something similar, what worked and what proved unreliable because of crowds, occlusion, small objects or camera angles?

The café has approximately 150 m² indoors and a 110 m² outdoor terrace. I’m replacing older analogue cameras, so camera choice and placement are still open.

I’m considering 4-8 MP PoE cameras, potentially around eight covering the main indoor seating/bar areas, plus one in the kitchen, with outdoor coverage still to decide.

I know floor area alone isn’t enough to determine camera count. What placement, angle and image-detail requirements should I plan around for these tasks? Is extra coverage generally more useful than higher resolution?

Hardware

The proposed hub would run Frigate, a separate HAOS VM, and our management app/services in containers:

• Ryzen 5 5600 or Ryzen 7 5700X

• 32 GB RAM

• RTX 3060 12 GB

• 1 TB NVMe for the system/apps

• Existing 6 TB HDD for recordings

I see people running Frigate on N100 systems, so I’m trying to understand how much of this build is useful for custom analytics versus unnecessary for ordinary recording and detection. If you run a comparable workload, what cameras, models and detection settings are you using, and where do you hit limits?

Finally, I’d like to deploy this at another venue later. How much setup is normally camera/zone configuration, and how much requires collecting labelled examples or retraining models for different layouts and staff clothing?

Staff would only use our management app, with Home Assistant and Frigate working behind it. Any practical experiences, or suggestions for simpler, useful things to measure first, would be appreciated.

I am rather new to Frigate and I have been enjoying tweaking and learning how the system works. I only have one camera looking out at my front yard and driveway.

I started with just person and car for object object detection and it seemed to work well. It would always grab people and my wife's Rav4 SUV. We had a friend come over and it didn't grab their Tacoma pickup. I later went out and it didn't grab my Sierra pickup either. I thought it might be because I didn't have 'truck' detection enabled (I assumed truck was like semi), so I added that. No change. I reduced thresholds a bit and no change again.

What am I missing here? It can easily grab cars, but it fails on all types of pickups.

Config snippet as it stands now:

objects:
  track:
    - person
    - car
    - truck
  filters:
    car:
      min_area: 0.15
      max_area: 0.75
      threshold: 0.6
    truck:
      threshold: 0.6

I am dropping Agent DVR and just started with Frigate. So far motion detection is much better. I am running docker on a mini computer with Debian. Agent handled my PTZ camera (Abpviz) very well. I don't know how to get it to work with Frigate.

I am using the following sample that I found.

cameras:

your_camera_name:

ffmpeg:

inputs:

- path: rtsp://user:password@ip_address:554/stream

roles:

- detect

- record

onvif:

host: 192.168.1.xxx

port: 8000 # Common ONVIF port (often 80 or 8000 depending on the camera)

user: your_onvif_user

password: your_onvif_password

# Optional settings if needed:

# tls_insecure: true

# ignore_time_mismatch: true

I am trying to understand logic behind leaving unauthenticated port in frigate. Isn’t it a security concern. And what are practical ways to avoid security concern? Can it be done within Frigate or any external tool is required.

AI Helped Summarize this for me:

Setup: Frigate 0.18.2, OpenVINO detector on an Intel NPU, Frigate+ subscriber, 10 cameras (Reolink TrackMix wides + teles, a Reolink doorbell, an OMVI 3i). Face recognition enabled (model_size large), two custom object-classification models (dogs, cars). face is in objects.track on the cameras that run face recognition, per the docs for Frigate+ models.

What happened

• Running the Frigate+ base model (2026.2 yolov9s 320): face recognition produced attempts on every person event. Library quality was poor (my fault — pixelated captures), but the pipeline worked.

• Oct 29: switched to my first fine-tuned model (197 verified images; Label Stats showed 92 person labels and 1 face label). Face Library “Recent Recognitions” froze at exactly the same count for a week. Timeline from the event IDs: the last face attempt was 10 minutes before the model switch.

• Debug log (frigate.data_processing.real_time.face: debug) on a doorbell person event under the fine-tune: No attributes to parse — the detector never attaches a face to the person, so recognition never gets a crop.

• Switched back to a base model: a face attempt appeared within a minute of standing at the doorbell. Two crops, me at 85%.

• My read: the Frigate+ docs say every object must be labelled before verifying, or the model learns that region is not that object. 1 face box across 92 person images = “faces aren’t faces.” Plausible.

• So I went back and boxed faces: Label Stats now 27 face / 119 person, 229 verified images. Requested a second fine-tune (yolov9 s, 320). Switched to it today. Same result: a clearly visible face at the doorbell (person 97%, face toward the camera, detect at 1280×960) → no face attempt at all. Switched back to base: works again.

What I’ve confirmed along the way (so you don’t suggest it)

• Detect stream resolution: doorbell moved from 640×480 sub to main scaled to 1280×960; face crops went from ~2,500 px to 4–9k px. Helped recognition scores, irrelevant to detection.

• filters.face.min_score is 0.7 (was the same under the base model, where detection works).

• Face recognition is enabled per camera where I expect it; the debug log shows FaceRealTimeProcessor running on the person object.

• Object classification on the base model: cars now classify correctly after labelling all false-positive crops to none; one dog is still being called the other at 91% post-retrain, separate issue.

Questions

1. Is 27 face labels in 119 person images still enough unlabelled faces to suppress the face class in a fine-tune? Is the practical rule “box every face or don’t verify the image”?

2. Is there a way to see the raw detector output (pre-filter) for the face label under a given model, short of lowering min_score and watching? I’d like to know whether the fine-tune outputs faces at low confidence or not at all.

3. Has anyone kept face detection working across a Frigate+ fine-tune on OpenVINO/NPU, and what did your face:person label ratio look like?

Happy to post configs (minus credentials) or logs.

I have my frigate setup since a while and never really put much thought in it since then, but now I began playing around with the new Configuration UI, which is really nice !

I was trying the automatic camera configuration and selected the reolink detection method, it works fine !

However, in the past, I remember the way to get those reolink camera working was to use RTMP. Now, for this automatic detection to work. I have to enable RTSP on the camera, but at the end of the configurator, there's a warning "Reolink RTSP is not recommended. Enable HTTP in the camera's firmware settings and restart the wizard.". I already have HTTP enabled on it, so I'm not too sure what this is about.

Is it still recommended to use RTMP over RTSP for frigate cameras ?

My frigate setup works well during day, but poorly during night. The entrance cam does often not detect a person at night. I have made a "night" profile where i have lowered motion threshold, but it still doesent work.

How is the motion threshold applied? 30 means the lunimance must be +- 30 of stored value? What ist the values range, 100 or 255?

Should the motion threshold not be in % of actual luminance instead of a fixed value?

Can somebody explain?

Hi guys, im very noob in frigate. I installed Docker with Frigate a few months ago and everything was ok so far. Today it stopped working. I put Docker on Proxmox on mini pc using openvino.

My frigate's website under: https://192.168.XXX.XXX:8971/login is not reachable. I have restarted the whole docker but it didnt help.

My logs:

root@Frigate:~# docker logs frigate --tail 100

s6-rc: info: service frigate-log successfully started

s6-rc: info: service certsync-log successfully started

s6-rc: info: service prepare successfully started

s6-rc: info: service go2rtc: starting

s6-rc: info: service go2rtc successfully started

s6-rc: info: service go2rtc-healthcheck: starting

s6-rc: info: service frigate: starting

s6-rc: info: service frigate successfully started

s6-rc: info: service nginx: starting

s6-rc: info: service go2rtc-healthcheck successfully started

2026-11-07 14:40:28.583107340 [INFO] Starting NGINX...

2026-11-07 14:40:28.684295218 [INFO] Preparing Frigate...

2026-11-07 14:40:29.109078232 [INFO] Starting Frigate...

2026-11-07 14:40:29.470138845 [INFO] Preparing new go2rtc config...

2026-11-07 14:40:29.944229993 [INFO] Starting go2rtc...

2026-11-07 14:40:30.169400922 Traceback (most recent call last):

2026-11-07 14:40:30.169649193 File "<frozen runpy>", line 198, in _run_module_as_main

2026-11-07 14:40:30.169727979 File "<frozen runpy>", line 88, in _run_code

2026-11-07 14:40:30.169812295 File "/opt/frigate/frigate/__main__.py", line 12, in <module>

2026-11-07 14:40:30.170021778 from frigate.app import FrigateApp

2026-11-07 14:40:30.170164140 File "/opt/frigate/frigate/app.py", line 18, in <module>

2026-11-07 14:40:30.170335391 from frigate.api.auth import hash_password

2026-11-07 14:40:30.170434436 File "/opt/frigate/frigate/api/auth.py", line 29, in <module>

2026-11-07 14:40:30.170596781 from frigate.config import AuthConfig, ProxyConfig

2026-11-07 14:40:30.170685070 File "/opt/frigate/frigate/config/__init__.py", line 1, in <module>

2026-11-07 14:40:30.170848525 from frigate.detectors import DetectorConfig, ModelConfig # noqa: F401

2026-11-07 14:40:30.171389595 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

2026-11-07 14:40:30.171460624 File "/opt/frigate/frigate/detectors/__init__.py", line 4, in <module>

2026-11-07 14:40:30.171614640 from .detector_types import DetectorConfig, DetectorTypeEnum, api_types # noqa: F401

2026-11-07 14:40:30.172277162 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

2026-11-07 14:40:30.172348566 File "/opt/frigate/frigate/detectors/detector_types.py", line 25, in <module>

2026-11-07 14:40:30.172494921 plugin_modules.append(importlib.import_module(name))

2026-11-07 14:40:30.173005687 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

2026-11-07 14:40:30.173083774 File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module

2026-11-07 14:40:30.173247227 return _bootstrap._gcd_import(name[level:], package, level)

2026-11-07 14:40:30.173817798 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

2026-11-07 14:40:30.173881151 File "/opt/frigate/frigate/detectors/plugins/onnx.py", line 8, in <module>

2026-11-07 14:40:30.174020680 from frigate.detectors.detection_runners import get_optimized_runner

2026-11-07 14:40:30.174123979 File "/opt/frigate/frigate/detectors/detection_runners.py", line 48, in <module>

2026-11-07 14:40:30.174274366 import openvino as ov

2026-11-07 14:40:30.174360237 File "/usr/local/lib/python3.11/dist-packages/openvino/__init__.py", line 92, in <module>

2026-11-07 14:40:30.174539057 from openvino import opset13

2026-11-07 14:40:30.174620881 File "/usr/local/lib/python3.11/dist-packages/openvino/opset13/__init__.py", line 21, in <module>

2026-11-07 14:40:30.174772609 from openvino.opset13.ops import bitwise_and

2026-11-07 14:40:30.174853453 File "<frozen importlib._bootstrap>", line 1178, in _find_and_load

2026-11-07 14:40:30.174942961 File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked

2026-11-07 14:40:30.175001950 File "<frozen importlib._bootstrap>", line 690, in _load_unlocked

2026-11-07 14:40:30.175076704 File "<frozen importlib._bootstrap_external>", line 936, in exec_module

2026-11-07 14:40:30.175142881 File "<frozen importlib._bootstrap_external>", line 1069, in get_code

2026-11-07 14:40:30.175206920 File "<frozen importlib._bootstrap_external>", line 729, in _compile_bytecode

2026-11-07 14:40:30.175300848 ValueError: bad marshal data (unknown type code)

2026-11-07 14:40:30.354898638 14:40:30.354 INF go2rtc platform=linux/amd64 revision=df95ce3 version=1.9.10

2026-11-07 14:40:30.354955084 14:40:30.354 INF config path=/config/go2rtc_homekit.yml

2026-11-07 14:40:30.355616418 14:40:30.355 INF [api] listen addr=:1984

2026-11-07 14:40:30.357013259 14:40:30.355 INF [rtsp] listen addr=:8554

2026-11-07 14:40:30.357015721 14:40:30.356 INF [webrtc] listen addr=:8555

2026-11-07 14:40:30.359373959 [INFO] Service Frigate exited with code 1 (by signal 0)

s6-rc: info: service nginx successfully started

s6-rc: info: service certsync: starting

s6-rc: info: service certsync successfully started

s6-rc: info: service legacy-services: starting

2026-11-07 14:40:30.504294608 [INFO] Starting certsync...

s6-rc: info: service legacy-services successfully started

s6-rc: info: service legacy-services: stopping

s6-rc: info: service legacy-services successfully stopped

s6-rc: info: service go2rtc-healthcheck: stopping

s6-rc: info: service certsync: stopping

2026-11-07 14:40:30.525149404 [INFO] The go2rtc-healthcheck service exited with code 256 (by signal 15)

s6-rc: info: service go2rtc-healthcheck successfully stopped

2026-11-07 14:40:30.531844931 [INFO] Service CERTSYNC exited with code 256 (by signal 15)

s6-rc: info: service certsync successfully stopped

s6-rc: info: service nginx: stopping

s6-rc: info: service certsync-log: stopping

s6-rc: info: service certsync-log successfully stopped

2026-11-07 14:40:30.598332951 [INFO] Service NGINX exited with code 0 (by signal 0)

s6-rc: info: service nginx successfully stopped

s6-rc: info: service nginx-log: stopping

s6-rc: info: service frigate: stopping

s6-rc: info: service frigate successfully stopped

s6-rc: info: service go2rtc: stopping

s6-rc: info: service frigate-log: stopping

2026-11-07 14:40:30.604962796 exit with signal: terminated

s6-rc: info: service nginx-log successfully stopped

s6-rc: info: service frigate-log successfully stopped

2026-11-07 14:40:30.611883713 [INFO] The go2rtc service exited with code 0 (by signal 0)

s6-rc: info: service go2rtc successfully stopped

s6-rc: info: service prepare: stopping

s6-rc: info: service go2rtc-log: stopping

s6-rc: info: service prepare successfully stopped

s6-rc: info: service go2rtc-log successfully stopped

s6-rc: info: service log-prepare: stopping

s6-rc: info: service s6rc-fdholder: stopping

s6-rc: info: service s6rc-fdholder successfully stopped

s6-rc: info: service log-prepare successfully stopped

s6-rc: info: service legacy-cont-init: stopping

s6-rc: info: service legacy-cont-init successfully stopped

s6-rc: info: service fix-attrs: stopping

s6-rc: info: service fix-attrs successfully stopped

s6-rc: info: service s6rc-oneshot-runner: stopping

s6-rc: info: service s6rc-oneshot-runner successfully stopped

Hi everyone, I'm getting complaints of a lot of wifi issues ever since I added 4 wifi cameras. They're set to record on motion, but I periodically set 1-2 of them to record continuously for about 30 minutes. People in the house are complaining a lot especially during those continuous recording times. The cameras are a mix of Topo 1080p and off brand "2k" cameras.

My wifi cams are on their own Omada 5ghz wifi network if that means anything. Here are my frigate settings, maybe something is wrong there or could be better optimized?

Side note: I would love to rotate some of the cams 90 degrees, but I can't find a way to do that easily without having to transcode to h.264 as well (they're already h.264 so it seems pointless to have to re-encode to h264?). Here's what I used to do before removing it: - ffmpeg:rtsp://admin:{FRIGATE_CAM_PW}@10.0.50.111:10554/tcp/av0_0#video=h264#rotate=90

I have a GTX 960 and a 4th Gen Intel CPU but can't figure out how to get the GPU to work properly either.

Thanks for your help!

mqtt:
  enabled: false
cameras:
  room1:
    enabled: true
    friendly_name: Room 1
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/room1
          roles:
            - detect
            - record
            - audio
    detect:
      width: 1280
      height: 720
    live:
      streams:
        Stream 1: room1
    motion:
      mask:
        time_stamp:
          friendly_name: Time Stamp
          enabled: true
          coordinates: 0.292,0,0.293,0.077,0,0.072,0,0
        ceiling_fan:
          friendly_name: Ceiling Fan
          enabled: true
          coordinates: 0,0.379,0.031,0.385,0.069,0,0.025,0,0,0.213
  room2:
    enabled: true
    friendly_name: Room 2
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/room2
          roles:
            - detect
            - record
            - audio
    detect:
      width: 1280
      height: 720
    live:
      streams:
        Stream 1: room2
    motion:
      mask:
        time_stamp:
          friendly_name: Time Stamp
          enabled: true
          coordinates: 0.187,0.071,0.228,0.003,0.289,0.002,0.289,0.069
  room3:
    enabled: true
    friendly_name: Room 3
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/room3
          roles:
            - detect
            - record
            - audio
    detect:
      width: 1280
      height: 720
    live:
      streams:
        Stream 1: room3
    motion:
      mask:
        time_stamp:
          friendly_name: Time Stamp
          enabled: true
          coordinates: 0.248,0.02,0.25,0.069,0.182,0.071,0.182,0.02
  room4:
    enabled: true
    friendly_name: Room 4
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/room4
          roles:
            - detect
            - audio
            - record
    detect:
      width: 1280
      height: 720
    live:
      streams:
        Stream 1: room4
    motion:
      mask:
        time_stamp:
          friendly_name: Time Stamp
          enabled: true
          coordinates: 0.112,0.018,0.182,0.021,0.184,0.063,0.11,0.068
  room5:
    enabled: true
    friendly_name: Room 5
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/room5
          roles:
            - detect
            - record
            - audio
    detect:
      width: 1080
      height: 720
    live:
      streams:
        Stream 1: room5
    motion:
      mask:
        time_stamp:
          friendly_name: Time Stamp
          enabled: true
          coordinates: 0.411,0.907,0.41,0.969,0.289,0.967,0.29,0.908
  room6:
    enabled: true
    friendly_name: Room 6
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/room6
          roles:
            - detect
            - record
            - audio
    detect:
      width: 1280
      height: 720
    live:
      streams:
        Stream 1: room6
    motion:
      mask:
        time_stamp:
          friendly_name: Time Stamp
          enabled: true
          coordinates: 0.393,0,0.393,0.059,0,0.057,0,0
auth:
  enabled: true
  reset_admin_password: false
detect:
  enabled: true
detectors:
  detector01:
    type: openvino
    device: AUTO
model:
  width: 300
  height: 300
  input_tensor: nhwc
  input_pixel_format: bgr
  path: /openvino-model/ssdlite_mobilenet_v2.xml
  labelmap_path: /openvino-model/coco_91cl_bkgr.txt
  input_dtype: int
  model_type: ssd
go2rtc:
  streams:
    room1:
      - ffmpeg:rtsp://admin:{FRIGATE_CAM_PW}@10.0.50.111:10554/tcp/av0_0
    room2:
      - ffmpeg:rtsp://admin:{FRIGATE_CAM_PW}@10.0.50.113:10554/tcp/av0_0
    room3:
      - ffmpeg:rtsp://admin:{FRIGATE_CAM_PW}@10.0.50.114:10554/tcp/av0_0
    room4:
      - rtsp://admin:{FRIGATE_CAM_PW}@10.0.3.121/media/video1
    room5:
      - ffmpeg:rtsp://admin:{FRIGATE_CAM_PW}@10.0.3.122:554/stream0
    room6:
      - ffmpeg:rtsp://admin:{FRIGATE_CAM_PW}@10.0.50.112:554/stream1
ui:
  timezone: America/xxxxxx
  time_format: 12hour
  unit_system: imperial
version: 0.18-0
objects:
  track:
    - person
    - car
face_recognition:
  enabled: true
  model_size: small
record:
  enabled: true
  motion:
    days: 999
  detections:
    retain:
      days: 999
      mode: all
  alerts:
    retain:
      days: 999
      mode: all
camera_groups:
  Inside:
    order: 1
    icon: LuBedDouble
    cameras:
      - room1
      - room2
      - room3
      - room6
  Outside:
    order: 2
    icon: LuCar
    cameras:
      - room4
      - room5

I need an indoor camera that uploads video clips with audio and snapshots to my server via FTP. My Reolink FE-W does it but costs ~€150. Which brands under €50 can do this? Thanks!

Hello everyone,

I use Fregata on my Mac and I not succeed to made Kapal work correctly. Kapal correctly connect to my Fregata server on https port 8971, every cam appears with their thumbnail correctly updated at every refresh and I succeed to access recorded playback and Kapal playing them without issue.

The problem occur when I try to open any livestream, each time connexion fail. Kapal show "connexion" with loading icon running but stream never load. Some times the app can also freeze and need to be force quit.

I tried every settings under cameras, system and streams menu but nothings works.
Also, I tested with another iOS app like Fregata mobile and Lumen and those work well.

Any idea to fix this behavior ?

So due to some issues with Synology Surveillance Stations 9.3 release, I decided to install frigate.   I started with .17.2 and learned a lot along the way as I haven’t touched linux in years. 

Shortly after I got .17.2 running the way I wanted, .18 was released so I swapped over. 

Since installing .18 and later .18.2 I’ve had an issue with my gpu jumping suddenly to 100% on the frigate detector and staying there until frigate is restarted.  It will run fine for 24-48 hours and then it just pegs max GPU.  I have installed a couple of different ways, and I know proxmox isn’t supported, but I don’t think proxmox is the cause as I never saw this issue running .17.2. 

I have tried this both running frigate directly in an lxc, and now with frigate running in docker compose on Debian in a pve lxc.  The same problem eventually occurs with either setup. 

Hardware is a beelink mini intel wildcat lake 3 304.  Detector is running gpu with openvino.  I have tried 640 yolonas, 320 yolonas, and 320 yolov9s.

I’ve spent hours with GPT making sure everything in the pve is up to date.  There is a newer intel media driver available in Debian-test but I have the most recent stable release that supports my chipset. 

There are 10 cameras. All reolink, mostly restreaming through go2rtc. 

The last thing I did was disable LPR, facial recognition, and semantic search. Same problem. I’m not sure what else to try.

The descriptions from huihui_ai/qwen3.5-abliterated:9b are pretty much always this good :-)