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Importing Neural Networks with ONNX
Importing Neural Networks with ONNX
This video explores the importance of the Open Neural Network Exchange (ONNX) project in machine learning and its benefits in model conversion across various tools. The speaker discusses the challenges of loading models manually or using automated tools and how ONNX eliminates this problem through its graph-based computational model. The speaker also highlights the advantages of ONNX in hand conversion of complex models and its compatibility with different frameworks. The video touches on parameterized net models, the structure of ONNX, and potential challenges that may arise when using the project. Despite these challenges, the speaker believes ONNX will thrive due to its substantial backing from various companies.
Importing and Exporting Neural Networks with ONNX
Importing and Exporting Neural Networks with ONNX
The video demonstrates the use of ONNX as a cross-platform specification and file format for machine learning models to exchange models between different neural network frameworks. The speakers show how to import and export neural networks using ONNX through Mathematica and Keras, and how to inspect and import metadata, as well as set metadata when exporting. They also discuss the exporting and importing of models between Core ML, PyTorch, and Wolfram Language, and the importance of using the correct offset during conversion. The speakers discuss the future of ONNX, including expanding support for import and export, improving challenging cases for the importer, and allowing exporting to multiple operator set versions. Additionally, the speaker explains the difference between ONNX and MXNet, and provides information on how to check which functions can be exported to ONNX using internal utilities.
Converting Tensorflow model to ONNX format - Human emotions detection
Converting Tensorflow model to Onnx format - Human emotions detection
The video discusses the benefits of converting pre-trained TensorFlow models to ONNX format, which provides a common format for representing machine learning models that can be interpreted across different hardware platforms using the ONNX runtime. By converting models, developers can run them more efficiently in different frameworks or use them more easily with other practitioners. The video demonstrates the process of converting TensorFlow and Keras models to ONNX format using tools and specifications provided on the ONNX GitHub repo, and highlights how the ONNX format optimizes the model and reduces runtime for predictions. The ONNX model also outperforms the TensorFlow model for human emotions detection on a CPU.
How to convert almost any PyTorch model to ONNX and serve it using flask
How to convert almost any PyTorch model to ONNX and serve it using flask
The video tutorial demonstrates how to convert a PyTorch model to ONNX format and serve it using Flask. The presenter starts with importing the dataset and defining the model using data parallel, followed by loading the model weights and exporting it to ONNX. The video showcases how to create a Flask endpoint to serve the ONNX model, followed by converting tensors to numpy arrays and obtaining the output from the model. The speaker also applies the sigmoid function to the model output to convert it to a probability between 0 and 1. Finally, they switch the device to CPU for fair comparison and demonstrate the faster response time of the API. The video concludes by noting that there are many ways to optimize ONNX models for improved performance and inviting viewers to share their feedback in the comments section.
How to convert PyTorch model to Tensorflow | onnx.ai | Machine Learning | Data Magic
How to convert PyTorch model to Tensorflow | onnx.ai | Machine Learning | Data Magic
In this video, the presenter demonstrates how to use the Open Neural Network Exchange (ONNX) library to convert a PyTorch model into a TensorFlow model. The benefits and usage of the ONNX library are discussed in detail, with a PyTorch model created to identify handwritten numbers used as an example. The process of training the model and converting it into the ONNX format is shown, before loading it into TensorFlow for prediction on sample images. The resulting TensorFlow model is saved as a .pb file, showcasing how the ONNX library can be used to convert any PyTorch model into TensorFlow.
How to convert Tensorflow model/tflite models to ONNX
How to convert Tensorflow model/tflite models to ONNX for importing it into unity
tf2onnx converts TensorFlow (tf-1.x or tf-2.x), tf.keras and tflite models to ONNX via command line or Python API.
https://github.com/onnx/tensorflow-onnx
Convert Pytorch (pytorch lightning) model to onnx model with variable batch size
Convert Pytorch (pytorch lightning) model to ONNX model with variable batch size
In this tutorial we will learn how Convert Pytorch (pytorch lightning) model to ONNX model with variable/dynamic batch size.
PyTorch ONNX Export Support - Lara Haidar, Microsoft
PyTorch ONNX Export Support - Lara Haidar, Microsoft
Lara Haidar from Microsoft explains the advantages of the PyTorch ONNX model export feature, allowing models to be moved from research to production and run on various hardware. She states that the ONNX runtime has become very popular, with millions of devices now using it and achieving notable performance gains. Moreover, the ONNX Export Support now includes improvements in model coverage, performance optimization, and backend support to ensure models can run on various versions with different backends. Finally, Lara encourages users to test the exported models and share feedback to enhance the feature further.
296 - Converting keras trained model to ONNX format - Image Classification example
296 - Converting keras trained model to ONNX format - Image Classification example
The video tutorial covers the process of converting a Keras trained image classification model to ONNX format for deployment. The speaker shows how to create a model using Keras, compile it, and save it as an H5 file before converting it to ONNX format. They provide a step-by-step guide on how to import the necessary libraries for ONNX conversion, how to load the saved H5 model, and how to convert it to ONNX format using a single line of code. The presenter then demonstrates how to use the resulting ONNX model in an ONNX runtime session, shows how to predict classes in an image classification example using ONNX, and compares the probabilities of the predictions using ONNX and Keras. The speaker emphasizes the effectiveness and advantages of using ONNX for deployment and notes the simplicity of converting an existing HDF file to ONNX.
297 - Converting keras trained model to ONNX format - Semantic Segmentation
297 - Converting keras trained model to ONNX format - Semantic Segmentation
This video focuses on converting a keras trained model to ONNX format for semantic segmentation of electron microscopy images of mitochondria. The presenter provides detailed steps on how to crop and load images, use data augmentation techniques, define generators for training and validation, train and save the model. The video also covers converting the model to ONNX format using the tf2onnx.convert library and using the ONNX model for prediction. The presenter highlights best practices for training and conversion and provides links to their previous videos on multi-class segmentation. The tutorial concludes with the presenter stating that this is the end of the ONNX series and they will focus on other topics in the next video.