nickswardh / yolodotnet Goto Github PK

Hi
I am using visual studio 2022 and the project is in .net8. The exception comes when loading the onnx model. I exported the model from .pt to .onnx in pytorch.

I am using nuget.

The exception is : 'The given key 'date' was not present in the dictionary.'

Please help

Hi !

I just asking if this package can do image classification, and how ?

Thanks ^^

Unhandled exception. System.Exception: There are more labels than available colors.
at YoloDotNet.Extensions.OnnxPropertiesExtension.MapLabelsAndColors(String onnxLabelData, ModelType modelType)
at YoloDotNet.Extensions.OnnxPropertiesExtension.GetOnnxProperties(InferenceSession session)
at YoloDotNet.Data.YoloBase..ctor(String onnxModel, Boolean useCuda, Int32 gpuId)
at YoloDotNet.Yolo..ctor(String onnxModel, Boolean cuda, Int32 gpuId)

Hi,

Do you have a version for Yolo nas?

Thx

Microsoft.ML.OnnxRuntime.OnnxRuntimeException: '[ErrorCode:RuntimeException] D:\a_work\1\s\onnxruntime\core\session\provider_bridge_ort.cc:1209 onnxruntime::ProviderLibrary::Get [ONNXRuntimeError] : 1 : FAIL : LoadLibrary failed with error 126 "" when trying to load "C:\Users\cyberpark\Documents\YoloDotNet-master\ConsoleDemo\bin\Debug\net8.0\runtimes\win-x64\native\onnxruntime_providers_cuda.dll"

_session = useCuda
? new InferenceSession(onnxModel, SessionOptions.MakeSessionOptionWithCudaProvider(gpuId))
: new InferenceSession(onnxModel);

OnnxModel = _session.GetOnnxProperties();

_parallelOptions = new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount };
_useCuda = useCuda;

in this case i got this problem after going to video progress section, can you assist?

There doesn't seem to be any IoU parameter from what I can see when I looked at the source code, I see that RemoveOverlappingBoxes uses a default value of 0.45f I think it would be useful if this was a parameter.

And maybe another IoU threshold parameter specifically if the overlapping boxes are only the same class, but I don't know if this is preferred. But I think at least one is very much needed.

Having the label index will be useful as I sometimes use it for further modification of my results.

Originally posted by @niclasastrom in #9 (comment)

Performance using the GPU is worse than using the CPU. I have an RTX 4070, running Windows 11 Pro. The latest OS- and NVidia driver updates are installed.

I expected higher throughput when using the GPU, but I could be wrong. What performance can I expect, CPU vs GPU?

For example, the classification test took 130ms on the CPU and 572ms on the GPU. Do you know if this is expected?

I added a couple of lines to measure compute time:

var stopWatch=new Stopwatch();
stopWatch.Start();
List<Classification> results = yolo.RunClassification(image, 3); // Get top 5 classifications. Default = 1
stopWatch.Stop();
Console.WriteLine("Elapsed time: "+stopWatch.ElapsedMilliseconds);

Thanks for your input. If this follow-up question doesn't fit the topic, please forgive me and I will try to file my question somewhere else.

Thankyou so much for your work!
The code works fine just a small issue in your code in YoloDotNet/Extensions/ImageExtensions.cs line 33
var tensor = new DenseTensor(new[] { inputBatchSize, inputChannels, height, width });

should be replaced with
var tensor = new DenseTensor(new[] { inputBatchSize, inputChannels, width, height });

This fixes the dimension error (else dim error will occur for images not having same width and height).

Hey great project, I've been tweaking it to use DirectML as the onnx execution provider because I'm one of the poor suckers who doesn't have a Nvidia GPU and it works very well as an abstraction on most GPUs.

If you're interested, I'd be happy to submit my patch thru a PR, although I couldn't seem to come up with a reasonable way to avoid breaking changes to the main Yolo constructor's call signature since it's currently designed for simple disabling of GPU use but let me know if you have any suggestions/preferences in that regard.

Cheers