CodeProject.AI Version 2.5

Yolo5 .NET installed properly but I get this error when 6.2 tried to install.

11:56:27:Started Object Detection (YOLOv5 6.2) module
11:56:29:detect_adapter.py: Traceback (most recent call last):
11:56:29:detect_adapter.py: File "C:\Program Files\CodeProject\AI\modules\ObjectDetectionYOLOv5-6.2\detect_adapter.py", line 19, in
11:56:29:detect_adapter.py: from codeproject_ai_sdk import JSON, ModuleRunner, LogMethod, LogVerbosity, RequestData
11:56:29:detect_adapter.py: ModuleNotFoundError: No module named 'codeproject_ai_sdk'
11:56:29:Module ObjectDetectionYOLOv5-6.2 has shutdown

Do I need 6.2?
You do not need 6.2 if .NET is working.
 
If they have an RTX3060 6.2 should offer much better performance right?

Here is another way to try and install.: Run an administrative command prompt and enter cd C:\Program Files\CodeProject\AI\modules\ObjectDetectionYOLOv5-6.2 (assumes default install location for CodeProject) and press enter. Then ../../setup and press enter. That should install the 6.2 via CLI.
 
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If they have an RTX3060 6.2 should offer much better performance right?

Here is another way to try and install.: Run an administrative command prompt and enter cd C:\Program Files\CodeProject\AI\modules\ObjectDetectionYOLOv5-6.2 (assumes default install location for CodeProject) and press enter. Then ../../setup and press enter. That should install the 6.2 via CLI.
No, I have a RTX 3060 in my main Blue Iris server and .NET is faster then 6.2

.NET
1727653380371.png

6.2
1727654006611.png
 
Hi guys, been a bit since I posted; life and all.
So I am not finding (I am sure its been discussed) anything current on what (if any) support there is for Intel ARC A750 (the Intel pcie cards) for acceleration.
I need to put BI/CPAI onto an AMD system with an Intel A750 and hoping by now these Intel add-in cards have support similar to the Intel iGPU.

BI/CPAI are on current/latest stable releases 5.9.7.4 & 2.6.5.0
Appreciate the assist.
 
The model was trained with dark & night images for better night detection, see the below link.


Do you use the main stream if available at night, or only for LPR?

My cams are struggling with detecting people at night for sure. Day is fine.
 
Last edited:
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I understand that Chris Maunder and Matthew Dennis are no longer with the Code Project AI Server project.
I am awaiting any word from the other two members of the group as to the future of the project.

Sent from my iPlay_50 using Tapatalk
 
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I understand that Chris Maunder and Matthew Dennis are no longer with the Code Project AI Server project.
I am awaiting any word from the other two members of the group as to the future of the project.

Sent from my iPlay_50 using Tapatalk
Let us know what you find out. Could be impacting to all the BI users.
Thanks
 
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I understand that Chris Maunder and Matthew Dennis are no longer with the Code Project AI Server project.
I am awaiting any word from the other two members of the group as to the future of the project.

Sent from my iPlay_50 using Tapatalk
Chris Maunder and Matthew Dennis are still going to be developing CodeProject.AI
They are moving everything off of CodeProject. Below are some of the new links.

1728513824912.png
1728513937242.png

 
IMG_4759.pngMoved to the ipcam-dark model for my cameras at night. Getting multiple person alerts with 80% accuracy now on this plant which I wasn’t before. Anything I can do or would I need to add this as an area to skip?
 
Chris Maunder and Matthew Dennis are still going to be developing CodeProject.AI
They are moving everything off of CodeProject. Below are some of the new links.

View attachment 204636
View attachment 204637

Oh. I was following the Code Project AI Server discussion board at codeproject.com.
I must have missed the transition announcement.
No wonder there are no replies there.
My bad.
I will search more carefully next time.

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Yes, you could create a zone in BI that excludes the area where the plant is. See zones and hotspot on page 103 of the help file.
Similar to PBC I have plants/tree branches that I have excluded due to false alerts but when its heavy rain or heavy winds, they can creep out into the rest of the monitoring window and trigger false again. Its a pain in the butt. To exclude the entire travel path of these limbs would effectively blind me to about 1/3 of the monitoring window. Its a less than stellar solution considering its supposed to have some sort of AI but cant seem to tell a swaying limb from animal/human :)
 
Similar to PBC I have plants/tree branches that I have excluded due to false alerts but when its heavy rain or heavy winds, they can creep out into the rest of the monitoring window and trigger false again. Its a pain in the butt. To exclude the entire travel path of these limbs would effectively blind me to about 1/3 of the monitoring window. Its a less than stellar solution considering its supposed to have some sort of AI but cant seem to tell a swaying limb from animal/human :)
If masking doesn’t work for you maybe try setting up multiple zones and utilize the “object crosses zones” option in object detections. Another option would be to increase the “min confidence (%)” until you find a happy medium.
 
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If masking doesn’t work for you maybe try setting up multiple zones and utilize the “object crosses zones” option in object detections. Another option would be to increase the “min confidence (%)” until you find a happy medium.
Thanks @Vettester , the multi-zones and crosses is a good idea. I can give that a try. Hate to raise the confidence as generally it works decent (minus the bad weather events that trigger falses out the wazoo).
 
Hi,

I am running 2.6.5 and using a single coral. My settings are efficientdet-lite and multi-tpu (though it says its falling back to single tpu).

I often get an error saying unable to run inference at least 1 reference to internal data. Any thoughts on this?

Capture.JPG
 
That's what happens when multiple threads hit the old single-TPU code at the same time (or it could be a bug in the multi-TPU code, but it sounds like that's not running). I'd be curious as to why it's falling back to single-TPU; fixing that and running the newer multi-TPU code would likely fix the problem.