Kayzwer

Results 23 comments of Kayzwer

@inisis [pull request](https://github.com/ultralytics/ultralytics/pull/12683) for the integration, thanks for the tool :)

> @Kayzwer Hi, CI failed, but I think it's not a problem with onnxslim, and how can I compare the performance with/without onnxslim, and one thing to mention, onnxslim can...

> > @Kayzwer Hi, CI failed, but I think it's not a problem with onnxslim, and how can I compare the performance with/without onnxslim, and one thing to mention, onnxslim...

> @inisis Thanks for the heads up! The PR is on Glenn's radar, and he'll review it as soon as possible. Thanks for your contribution! 🚀 thanks LLM

> @Kayzwer Hello, thx for your effort! What do you think, what is better to use - [onnx-simplifier](https://github.com/daquexian/onnx-simplifier.git) or [onnx-slim](https://github.com/tsingmicro-toolchain/OnnxSlim.git)? try both

> @Kayzwer Hello, thx for your effort! What do you think, what is better to use - [onnx-simplifier](https://github.com/daquexian/onnx-simplifier.git) or [onnx-slim](https://github.com/tsingmicro-toolchain/OnnxSlim.git)? also thanks to @inisis for developing the tool

> @inisis @Kayzwer thanks guys! Do you have benchmarks or info on i.e. YOLOv8n models exported with both [onnx-simplifier](https://github.com/daquexian/onnx-simplifier.git) and [onnx-slim](https://github.com/tsingmicro-toolchain/OnnxSlim.git)? > > Are the number of layers, operations or...

> > > > @inisis @Kayzwer thanks guys! Do you have benchmarks or info on i.e. YOLOv8n models exported with both [onnx-simplifier](https://github.com/daquexian/onnx-simplifier.git) and [onnx-slim](https://github.com/tsingmicro-toolchain/OnnxSlim.git)? > > > > Are the...

> > @initialencounter hey, thanks for adding to the discussion! Regarding applying onnx-simplifier and onnx-slim sequentially, you can definitely try using both tools on a model to maximize optimization. Each...

> Hi, @Kayzwer there is a conflict here, can you help fix it, and I want to make onnxslim as default option, because it's faster. just make slim to true...