What’s your opinion of people dropping capital letters at the beginning of sentences over the years, especially in informal online spaces?

I just noticed one more thing. You confidently say that the current record is held by Apollo 13. In the link you provided earlier (https://en.wikipedia.org/wiki/Apollo_13#cite_note-130), it says they only beat Apollo 10 by 240 km. If 240 km is good enough for you to believe 13 holds the record, then how come you don’t want to accept the upcoming record with a difference of 6602 km?

If the moon wasn’t adjacent to their path, would they ever get pulled back to earth by earths gravity?

Thanks for the explanation. It’s an odd combination of pedantry and ignoring facts.

Apollo 13 - 400,171 km (248,655 miles)
Artemis 2 - 406,773 km (252,757 miles)

One of those is clearly further.

I tend to agree about language changing, presidents not having speech writers because they can’t read anyway, etc., but in the case of “not breaking a record because it’s not a high enough margin” I have to completely disagree.

Sorry, if they break the record by 1%, but also “no, they are not”, then which one is it?

I know it depends on timing and stuff as far as their furthest distance from Earth. Is it the “distance travelled” statistic? Like actual mileage flown as opposed to absolute distance from Earth?

Such a cool photo with the auroras.

Very expensive… But the Nuwave is very nicely built and nearly silent on the lower settings. It comes with enough filters to last you 5+ years. Replacements after that seem to be $20 each, and they last a year.

I don’t use the wifi function, but it’s a thing.

The price has gone up a lot since I bought it. Would be hard to justify now.

Was it windy? How did it end up behind the conveyor?

The article likely isn’t written about you.

Is this what you have? https://www.geapplianceparts.com/store/parts/spec/WD08X23476

Found it via the “body” parts diagram from the link you posted.

RepairClinic has a good video. It looks super easy. https://www.repairclinic.com/PartDetail/Door-Seal/WD08X23476/4466530

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https://cluesbysam.com/

I half agree with you. I shower daily but only use soap on my pits and bits. If I’ve done yardwork or exercise I’ll hit everything with soap, though.

You didn’t ask me, but as someone who has fiddled with these things my favorite sensor is the BME680, specifically the board that Adafruit sells. It costs more than other sensors, but that’s because it’s the best. It also does temperature, atmospheric pressure, and measures VOC. I think you can even use it to detect when someone takes a poo, but I haven’t tried.

I connect mine to LOLIN D1 Minies running Tasmota.

That’s wonderful.

I will miss the auto-post on the 9th of each month.

Thanks for the chuckles.

Sweet!

I don’t have an external GPU either, just the onboard Intel graphics is what I use now. Also worth mentioning to use integrated graphics your Docker Compose needs:

devices:
      - /dev/dri/renderD128:/dev/dri/renderD128

I’m not using substreams. I have 2 cameras and the motion detection doesn’t stress the CPU too much. If I add more cameras I’d consider using substreams for motion detection to reduce the load.

Your still frames in Home Assistant are the exact problem I was having. If your cameras really do need go2rtc to reduce connections (my wifi camera doesn’t seem to care), you might try changing your Docker container to network_mode: host and see if that fixes it.

Here’s my config. Most of the notations were put there by Frigate and I’ve de-identified everything. Notice at the bottom go2rtc is all commented out, so if I want to add it back in I can just remove the #s. Hope it helps.

mqtt:
  enabled: true
  host: <ip of Home Assistant>
  port: 1883
  topic_prefix: frigate
  client_id: frigate
  user: mqtt username
  password: mqtt password
  stats_interval: 60
  qos: 0

cameras:     # No cameras defined, UI wizard should be used
  baby_cam:
    enabled: true
    friendly_name: Baby Cam
    ffmpeg:
      inputs:
        - path: 
            rtsp://user:pw@<ip-addr>:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
          roles:
            - detect
            - record
      hwaccel_args: preset-vaapi
    detect:
      enabled: true # <---- disable detection until you have a working camera feed
      width: 1920 # <---- update for your camera's resolution
      height: 1080 # <---- update for your camera's resolution
    record:
      enabled: true
      continuous:
        days: 150
      sync_recordings: true
      alerts:
        retain:
          days: 150
          mode: all
      detections:
        retain:
          days: 150
          mode: all
    snapshots:
      enabled: true
    motion:
      mask: 0.691,0.015,0.693,0.089,0.965,0.093,0.962,0.019
      threshold: 14
      contour_area: 20
      improve_contrast: true
    objects:
      track:
        - person
        - cat
        - dog
        - toothbrush
        - train

  front_cam:
    enabled: true
    friendly_name: Front Cam
    ffmpeg:
      inputs:
        - path: 
            rtsp://user:pw@<ip-addr>:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
          roles:
            - detect
            - record
      hwaccel_args: preset-vaapi
    detect:
      enabled: true # <---- disable detection until you have a working camera feed
      width: 2688 # <---- update for your camera's resolution
      height: 1512 # <---- update for your camera's resolution
    record:
      enabled: true
      continuous:
        days: 150
      sync_recordings: true
      alerts:
        retain:
          days: 150
          mode: all
      detections:
        retain:
          days: 150
          mode: all
    snapshots:
      enabled: true
    motion:
      mask:
        - 0.765,0.003,0.765,0.047,0.996,0.048,0.992,0.002
        - 0.627,0.998,0.619,0.853,0.649,0.763,0.713,0.69,0.767,0.676,0.819,0.707,0.839,0.766,0.869,0.825,0.889,0.87,0.89,0.956,0.882,1
        - 0.29,0,0.305,0.252,0.786,0.379,1,0.496,0.962,0.237,0.925,0.114,0.879,0
        - 0,0,0,0.33,0.295,0.259,0.289,0
      threshold: 30
      contour_area: 10
      improve_contrast: true
    objects:
      track:
        - person
        - cat
        - dog
        - car
        - bicycle
        - motorcycle
        - airplane
        - boat
        - bird
        - horse
        - sheep
        - cow
        - elephant
        - bear
        - zebra
        - giraffe
        - skis
        - sports ball
        - kite
        - baseball bat
        - skateboard
        - surfboard
        - tennis racket
      filters:
        car:
          mask:
            - 0.308,0.254,0.516,0.363,0.69,0.445,0.769,0.522,0.903,0.614,1,0.507,1,0,0.294,0.003
            - 0,0.381,0.29,0.377,0.284,0,0,0
    zones:
      Main_Zone:
        coordinates: 0,0,0,1,1,1,1,0
        loitering_time: 0

detectors: # <---- add detectors
  ov:
    type: openvino
    device: GPU

model:
  model_type: yolo-generic
  width: 320 # <--- should match the imgsize set during model export
  height: 320 # <--- should match the imgsize set during model export
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/yolov9-t-320.onnx
  labelmap_path: /labelmap/coco-80.txt

version: 0.17-0


#go2rtc:
#  streams:
#    front_cam:
#      - ffmpeg:rtsp://user:pw@<ip-addr>:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
#    baby_cam:
#      - ffmpeg:rtsp://user:pw@<ip-addr>:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif

That CPU has UHD Graphics 750 which is newer than mine which has 730. Should work quite nicely.

Are you using Proxmox, too?

Sounds like LXC is the way to go to pass a Coral through. Not sure why it’s so flaky with the Debian VM.

I’ll keep an eye out for that. So far the Inference Speed is holding stead at 8.47ms.

Are you using OpenVINO with the onboard GPU, or CPU? I think it works with both so you need to make sure it’s using the GPU if possible.

That’s good to hear. That reinforces my suspicion that my problems were caused by passing it through to the virtual machine using Proxmox.

You might be interested in trying to enable the YOLOv9 models. The developer claims they are more accurate, and so far I’m tempted to agree.