To see how the dme-compunet, updated YoloDotNet and sstainba NuGets performed on an ARM64 CPU I built a test rig for the different NuGets using standard images and ONNX Models.

I started with the dme-compunet YoloV8 NuGet which found all the tennis balls and the results were consistent with earlier tests.

The YoloDotNet by NickSwardh NuGet update had some “breaking changes” so I built “old” and “updated” test harnesses.

The YoloDotNet by NickSwardh V1 and V2 results were slightly different. The V2 NuGet uses SkiaSharp which appears to significantly improve the performance.

Even though the YoloV8 by sstainba NuGet hadn’t been updated I ran the test harness just in case

The dme-compunet YoloV8 and NickSwardh YoloDotNet V1 versions produced the same results, but the NickSwardh YoloDotNet V2 results were slightly different.

• dme-Compunet 291 mSec
• NickSwardV1 480 mSec
• NickSwardV2 115 mSecs
• SStainba 422 mSec

Like in the YoloV8-NuGet Performance X64 CPU post the NickSwardV2 implementation which uses SkiaSharp was significantly faster so it looks like Sixlabors.ImageSharp is the issue.

To support Compute Unified Device Architecture (CUDA) or TensorRT inferencing with NickSwardV2(for SkiaSharp) will need some major modifications to the code so it might be better to build my own YoloV8 Nuget.