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Could you please send a copy of the system info and the module info
"Mistakes are prevented by Experience. Experience is gained by making mistakes."
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System Info:
Server version: 2.5.4
System: Windows
Operating System: Windows (Microsoft Windows 11 version 10.0.22631)
CPUs: Intel(R) Core(TM) i9-10850K CPU @ 3.60GHz (Intel)
1 CPU x 10 cores. 20 logical processors (x64)
GPU (Primary): NVIDIA GeForce GTX 1650 (4 GiB) (NVIDIA)
Driver: 522.30, CUDA: 11.8 (up to: 11.8), Compute: 7.5, cuDNN: 8.9
System RAM: 32 GiB
Platform: Windows
BuildConfig: Release
Execution Env: Native
Runtime Env: Production
.NET framework: .NET 7.0.10
Default Python:
Video adapter info:
NVIDIA GeForce GTX 1650:
Driver Version 31.0.15.2230
Video Processor NVIDIA GeForce GTX 1650
System GPU info:
GPU 3D Usage 24%
GPU RAM Usage 2.2 GiB
Global Environment variables:
CPAI_APPROOTPATH = <root>
CPAI_PORT = 32168
Module Info:
Module 'Object Detection (YOLOv5 .NET)' 1.9.3 (ID: ObjectDetectionYOLOv5Net)
Valid: True
Module Path: <root>\modules\ObjectDetectionYOLOv5Net
AutoStart: True
Queue: objectdetection_queue
Runtime: dotnet
Runtime Loc: Shared
FilePath: bin\ObjectDetectionYOLOv5Net.exe
Pre installed: False
Start pause: 1 sec
Parallelism: 0
LogVerbosity:
Platforms: all
GPU Libraries: installed if available
GPU Enabled: enabled
Accelerator:
Half Precis.: enable
Environment Variables
CUSTOM_MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\custom-models
MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\assets
MODEL_SIZE = medium
Module 'Object Detection (YOLOv5 .NET)' 1.9.3 (ID: ObjectDetectionYOLOv5Net)
Valid: True
Module Path: <root>\modules\ObjectDetectionYOLOv5Net
AutoStart: True
Queue: objectdetection_queue
Runtime: dotnet
Runtime Loc: Shared
FilePath: bin\ObjectDetectionYOLOv5Net.exe
Pre installed: False
Start pause: 1 sec
Parallelism: 0
LogVerbosity:
Platforms: all
GPU Libraries: installed if available
GPU Enabled: enabled
Accelerator:
Half Precis.: enable
Environment Variables
CUSTOM_MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\custom-models
MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\assets
MODEL_SIZE = medium
Module 'Object Detection (YOLOv5 .NET)' 1.9.3 (ID: ObjectDetectionYOLOv5Net)
Valid: True
Module Path: <root>\modules\ObjectDetectionYOLOv5Net
AutoStart: True
Queue: objectdetection_queue
Runtime: dotnet
Runtime Loc: Shared
FilePath: bin\ObjectDetectionYOLOv5Net.exe
Pre installed: False
Start pause: 1 sec
Parallelism: 0
LogVerbosity:
Platforms: all
GPU Libraries: installed if available
GPU Enabled: enabled
Accelerator:
Half Precis.: enable
Environment Variables
CUSTOM_MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\custom-models
MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\assets
MODEL_SIZE = medium
Status Data: {
"inferenceDevice": "GPU",
"inferenceLibrary": "DirectML",
"canUseGPU": true,
"successfulInferences": 132799,
"failedInferences": 0,
"numInferences": 132799,
"averageInferenceMs": 51,
"histogram": {
"person": 50202,
"dog": 2778,
"cat": 13447,
"bird": 918,
"pig": 4,
"vehicle": 124,
"horse": 1,
"car": 46,
"bear": 24,
"squirrel": 2,
"truck": 13,
"rabbit": 2,
"bus": 1,
"Person": 21,
"DayPlate": 2,
"Dog": 16
},
"numItemsFound": 67601
}
Started: 23 Feb 2024 4:59:16 PM GMT Standard Time
LastSeen: 27 Feb 2024 6:54:41 PM GMT Standard Time
Status: Started
Requests: 132804 (includes status calls)
Installation Log
2024-02-17 11:22:07: Installing CodeProject.AI Analysis Module
2024-02-17 11:22:07: ======================================================================
2024-02-17 11:22:07: CodeProject.AI Installer
2024-02-17 11:22:07: ======================================================================
2024-02-17 11:22:07: 795.0Gb of 952Gb available on System
2024-02-17 11:22:07: General CodeProject.AI setup
2024-02-17 11:22:07: Creating Directories...Done
2024-02-17 11:22:07: GPU support
2024-02-17 11:22:08: CUDA Present...Yes (CUDA 11.8, cuDNN 8.9)
2024-02-17 11:22:08: ROCm Present...No
2024-02-17 11:22:09: Reading ObjectDetectionYOLOv5Net settings.......Done
2024-02-17 11:22:09: Installing module Object Detection (YOLOv5 .NET) 1.9.3
2024-02-17 11:22:15: Downloading ObjectDetectionYOLOv5Net-DirectML-1.9.3.zip...Expanding...Done.
2024-02-17 11:22:15: Copying contents of ObjectDetectionYOLOv5Net-DirectML-1.9.3.zip to bin...done
2024-02-17 11:23:00: Downloading YOLO ONNX models...Expanding...Done.
2024-02-17 11:23:00: Copying contents of yolonet-models.zip to assets...done
2024-02-17 11:23:37: Downloading Custom YOLO ONNX models...Expanding...Done.
2024-02-17 11:23:37: Copying contents of yolonet-custom-models.zip to custom-models...done
2024-02-17 11:23:39: Self test: Self-test passed
2024-02-17 11:23:39: Module setup time 00:01:31.49
2024-02-17 11:23:39: Setup complete
2024-02-17 11:23:39: Total setup time 00:01:31.95
Installer exited with code 0
P.S. Had to install .NET version as CUDA kept failing every 10-12 hours with memory access issues as per this post:
CodeProject.AI Server: AI the easy way.[^]
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Maybe I'm missing something in the documentation, but I can't seem to find anything about any kind of authentication whatsoever. No passwords, no API keys, nothing. Given that this is clearly designed to be a network resource, has some resources to support that, and the documentation guides you to configurations where the server listens on wildcard (all addresses on the machine), how are you intended to secure it? Is it just assumed that your using namespaces and firewall rules to isolate it?
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1:
V2.5.1 Single TPU Coral USB on Proxmox host -> passthrough to Container -> passthrough to Docker image:
V2.5.4 on Win11 with Dual TPU m.2 Adapter:
Why 2.5.1 detect more person?
2:
V2.5.1 Single TPU Coral USB on Proxmox host -> passthrough to Container -> passthrough to Docker image:
V2.5.4 on Win11 with Dual TPU m.2 Adapter:
Why doenst detect 2.5.4 the Person on the bicycle and why doesnt detect 2.5.1 the bicycle but the Person?
iv tryed all 3 Models on 2.5.4, but no changes.
BI detect on 2.5.4 my car as a sink and a vent pipe/flowerpot as a toilet. With 2.5.1 is the detection more precise.
here with 2.5.1
What can i do to get a better precision?
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The MobileNet models are the oldest ones. Are you able to use the YOLO models? They are newer and should perform the best. Next I would experiment with using a model larger than small.
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iv changed to Medium and Yolo, but why is on the Explorer Yolo5 and on the Module Setting Yolo8? whats correct?
must i change something on BLueiris too?
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Looks like one of the numbers wasn’t changed in the interface. How is it working for you?
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NOt really...
My car is a TV
My Heatpump is a car
My trash is a suitcase or a toilet
same picture on 2.5.1 and 2.5.4
2.5.1 looks good
and here with 2.5.4, not good
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Could you post some of the originals so I can run some tests on them?
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here is it
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Here are all the models. cars_test.zip - Google Drive[^]
Notes:
All models were run on a Coral TPU with an 8-bit model.
The non-YOLO models were downloaded from the Coral example models page.
The input image has been stretched to fit the input tensor of the model. So 300x300 or 640x640, etc. One YOLOv8 model has an input that was stretched during model creation to already fit a landscape aspect ratio.
If the file is missing, nothing was found
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I would tend to agree with you!
Using the v2.5.6 and a Coral with v2.1.4 - I have tried using Yolo, MobileNet and EfficientDet, I've had all sorts of results, ranging from Teddy Bear, TV, surfboard
I've gone back to using YOLLOv4 .NET on my CPU which is at least picking me up when I walk out of my front door, previously I wasn't getting anything
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Hello,
how can i see if all my TPUs are used?
I have one Dual TPU in M.2 and one USB TPU. Is there a way to see if all 3 TPUs are Working? Log show me only TPU Detected...
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I’m working on a new version of the TPU code and I’ll add that.
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sounds good release foreseeable ?
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I’m trying out a new idea and have some other things going on, so probably not for at least a few weeks.
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I switched the Coral model to EfficientNet-Lite. The detections are fast and somewhat acceptable in their quality, but nearly every time CPAI is called from Blue Iris, I’m getting “AI: error 500” messages from Blue Iris. It’s spamming my phone since it’s an error.
I have facial recognition turned off in Blue Iris. I have removed all face processing modules from CPAI. This doesn’t seem to happen with the MobileNet and YoloV8 modules on the Coral.
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Are you trying to use custom models? The Coral module doesn't include any custom models
cheers
Chris Maunder
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No, no custom models. Just changed the model in the CPAI Coral settings JSON from MobileNet to EfficientNet-Lite.
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Same thing with the default model (MobileNet SSD) actually. So it's not EfficientNet-Lite, it's just... something in general.
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Hi
im currently experiencing some issues with Codeproject which keeps restarting or times out. this happens multiple times a day and sometimes need to restart the service
Im running Blueiris (5.8.77) and Codeproject (2.5.4) on a Windows 11 (i5-9500) with Nvidia Quadro P620 (Driver: 551.52, CUDA: 12.4 (up to: 12.4), Compute: 6.1, cuDNN: 8.5)
From the logs of Codeproejct i see line "Sending shutdown request to python/ObjectDetectionYOLOv5-6.2" without anything triggering the shutdown call..
and in Blueiris a lot of entries with following value:
AI: Could not be restarted
or
AI: restarted successfully
While this is happening the cpu and ram is under 20 % while the gpu is 2 %
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Change Blue Iris settings so it doesn't start and stop CodeProject.AI
cheers
Chris Maunder
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I had the same exact issue, so I reverted back to 2.3.4, and had no restarts since. Will this get fixed on the next version update?
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Hello, codeproject/ai-server:gpu is outdated (2023-09-18) and asking me to update, are there going to be updates to this?
2. I am curious why codeproject/ai-server:gpu and codeproject/ai-server are separate as both can use CPU processing and it would probably mean less work if they were merged together.
Thank you for this project, it is a life changer and I appreciate all that you do.
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wabash11 wrote: are there going to be updates to this
Updates to the latest version? We're always updating, so yes.
wabash11 wrote: why codeproject/ai-server:gpu and codeproject/ai-server are separate
The GPU image is twice the size of the CPU, so the CPU is provided for those who know they won't need the GPU bloat.
(and: you're very welcome!)
cheers
Chris Maunder
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