Recent content by Pentagano

Always tweaking the settings tbh - at the moment most exterior cams have this set in cpai. Had it set to 20 post triggers before - but this makes no difference on the gpu objects and % confidence vary a little in each cam. On a busy day it can go for a long time and rarely exit P8 state

All of the time 6 of the exterior cameras. Sometimes the interior ones when I'm out. FPS all pretty low but high fps is not really needed for the analysis But 6 of them using the gpu - it flies through the images at less than 50ms 13:28:11:Response rec'd from Object Detection (YOLOv5 3.1)...

Most of the time my gpu gtx970 is at idle in P8 state at about 24w the unraid plugin says. Maybe less than that I've read If it runs at 24w over 24 hours - calculated extra electricity cost 3 pesos here (day is split into 3 different costs depending on the hour!) 11 pesos per Kwh for 4 hours 5...

Same here - Can't get it to send any alerts, mqtt for debugging. Set it to 3 seconds and low sensitivity also. Can't see any triggers using the rectangles while watching but does not alert me for non-detection

I have only got amd cpu's and use a gpu. But I would like to know from users with intel processors which ones that can achieve inference speeds of 40-100ms with yolov5 medium size models. Thanks

BlueIris license blocked trying to reapply an old config!! sent email to support. Anyways with my gtx970 I have found YOLOv5 3.1 to be most effective. Tried v8.0 and it did not pickup small animals possibly due to the lack of custom models? Only had general. 3.1 has ipcam-combined which in my...

After several days of tweaking and some frustration I may give in and put my gpu back in. Can't fault the tpu speed and low power consumption but the models are just not working as I want them to. Fine if you just want to identify people in good light. My nvidia gpu was spot on 90% of the time.

I'm trying out EfficientNet for a couple of days Difference between SSD MobileNet, EfficientNet and Faster R-CNN ResNet 50 | by Elven Kim | Medium

Nice! They've made decent improvements as now it appears to work. Still buggy though - not sure what model it is using really as the dashboard always says mobilenet SSD. I switched to yolov5 but all inference speeds seems to be the same for mobilenet, yolov8 and yolov5. I'd like to know how...

If I find another model .tflite file on git how do I program cpai to use that specific custom model? I'm at the bottom of the learning curve..

So coming soon then possibly?

Can these models be used with your coral tpu or just Nvidia gpu/cpu?

very true - sometimes a compromise - a small fluffy dog or cat may be recognised as a sheep or rabbit on the small model but not at all on the medium - large model. I'm trying to understand how to use some customized models from git but do not understand how to implement and use them yet like...

Some interesting results testing the tiny, small, medium and large MobileNet SSD with the same picture. The small model found far more objects that all the other models even though some were wrong!

One odd observation is using the dashboard. Even if I switch model and under the info it says yolov8 or Efficientdetlite for example - the dashboard only ever gives me the option to test with mobilenet ssd. What do you observe?