tensorflow-onnx
onnx
tensorflow-onnx | onnx | |
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7 | 38 | |
2,228 | 16,959 | |
1.7% | 1.6% | |
7.1 | 9.5 | |
about 1 month ago | 4 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
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tensorflow-onnx
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Operationalize TensorFlow Models With ML.NET
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
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Which models can be converted to 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.
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10$ Full Body Tracking! I'm proud to release ToucanTrack (in Beta!). Get decent FBT with the power of 2 PS3 Eye Cameras and AI!
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!
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Auto Annotation using ONNX and YOLOv7 model (Object Detection)
pb to ONNX Follow tensorflow-onnx:- https://github.com/onnx/tensorflow-onnx
- Can you inference a .tflite model file using Pytorch mobile?
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💊Your daily dose of machine learning : converting deep learning models to ONNX format
You can learn more about this tool on their github repo : https://github.com/onnx/tensorflow-onnx
onnx
- Onyx, a new programming language powered by WebAssembly
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From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
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.
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Object detection with ONNX, Pipeless and a YOLO model
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.
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38TB of data accidentally exposed by Microsoft AI researchers
ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.
[0] https://github.com/onnx/onnx
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Reddit’s LLM text model for Ads Safety
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.
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Operationalize TensorFlow Models With ML.NET
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.
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.
[0] https://onnx.ai
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Does ONNX Runtime not support Double/float64?
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
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Async behaviour in python web frameworks
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.
- Pickle safety in Python
What are some alternatives?
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
stable-diffusion-webui - Stable Diffusion web UI
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
CFU-Playground - Want a faster ML processor? Do it yourself! -- A framework for playing with custom opcodes to accelerate TensorFlow Lite for Microcontrollers (TFLM). . . . . . Online tutorial: https://google.github.io/CFU-Playground/ For reference docs, see the link below.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
stable-diffusion - A latent text-to-image diffusion model
infery-examples - A collection of demo-apps and inference scripts for various deep learning frameworks using infery (Python).
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]