tensorflow-onnx VS onnx

The easiest way to transform the downloaded TensorFlow model to an ONNX model is to use the tool tf2onnx from https://github.com/onnx/tensorflow-onnx

But I found that there are limitation in practice. For instance, I found that the conversion of this model to ONNX fails when using tf2onnx.

They come in the form of tflite models, so I had to convert them to onnx. I used tf2onnx for converting the pose landmark model and tflite2tensorflow for converting the pose detection model. For improving performance, I had created a small script which modified the landmark models for supporting batch inference. This script is not included in the repository, but do tell me if you need it!

pb to ONNX Follow tensorflow-onnx:- https://github.com/onnx/tensorflow-onnx

You can learn more about this tool on their github repo : https://github.com/onnx/tensorflow-onnx

Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.

ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.

ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.

[0] https://github.com/onnx/onnx

Running inference for large models on CPU is not a new problem and fortunately there has been great development in many different optimization frameworks for speeding up matrix and tensor computations on CPU. We explored multiple optimization frameworks and methods to improve latency, namely TorchScript, BetterTransformer and ONNX.

ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.

I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.

[0] https://onnx.ai

It's not clear why you thing this sub is appropriate for some third party system with a Python interface. Why don't you try their discussion group: https://github.com/onnx/onnx/discussions

This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.