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CCTV4 Feedback #97

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opened 2024年08月16日 21:25:10 +02:00 by Curid · 8 comments
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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?

model files

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? [model files](https://codeberg.org/Curid/TF-CCTV/src/branch/master/models/cctv4)

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.. ☹️

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.. ☹️
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Give yolov9s a try, you'll just have to apply this patch to Frigate.

Give [yolov9s](https://codeberg.org/Curid/TF-CCTV/src/branch/master/yolo/models_official/yolov9s) a try, you'll just have to apply [this](https://codeberg.org/Curid/TF-CCTV/pulls/102) patch to Frigate.

@Curid I'm using Coral (usb), is that supported? Frigate documents shows no.

@Curid I'm using Coral (usb), is that supported? Frigate documents shows no.
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Should be

Models ending in "_edgetpu.tflite" are compiled for the Google Coral EdgeTPU. Models without "edgetpu" (simply ".tflite") are for CPU use.

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.

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.." 🥲

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?

@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?
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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

We have a few training script on the [temp branch](https://codeberg.org/Curid/TF-CCTV/src/branch/temp/yolo/models) 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](https://app.element.io/#/room/#tf-cctv:matrix.org)
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