Do you notice any improvement in accuracy both day and night compared to other models?
Is the slower inference speed acceptable, and should the next model sacrifice more speed for better accuracy?
Do you notice any improvement in accuracy both day and night compared to other models?
Is the slower inference speed acceptable, and should the next model sacrifice more speed for better accuracy?
I was getting too many false positive on the default model provided by Frigate. Google brought me here, and I decided to give cctv4 a shot but still getting false positive.. ☹️
@Curid I'm using Coral (usb), is that supported? Frigate documents shows no.
Should be
Models ending in "_edgetpu.tflite" are compiled for the Google Coral EdgeTPU. Models without "edgetpu" (simply ".tflite") are for CPU use.
I've been using your recommended model, and I longer get dogs recognized at person even at night, yay..! 🎉 thank you so much.
Reminded me the saying: "never judge too quickly.." 🥲
@Curid I’m getting too many false positives — is there a way to fine-tune the YOLOv9s model? Any tutorial or guide you’d recommend?
We have a few training script on the temp branch that are ready to run if someone with a good GPU wants to do it.
There's more information about it in the Matrix room
No due date set.
No dependencies set.
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