Model optimizer openvino tensorflow. For a TensorFlow model, the layout is [N,C,H,W].

Model optimizer openvino tensorflow 0-rc1) with transfer learning method using ResNet50. x, and ONNX. 3. (Full error message at bottom) Dec 23, 2020 · Fortunately, OpenVINO Model Optimizer has built-in support for TensorFlow model conversion. The instructions are different depending on whether your model was created with TensorFlow v1. Below the command used Fortunately, OpenVINO Model Optimizer has built-in support for TensorFlow model conversion. 0. It supports methods, like Quantization-aware Training and Filter Pruning. OpenVINO optimizes the TensorFlow model and provides faster inference showed OpenVINO can be learned by anyone, including data scientists, software developers, and AI/ML engineers. Jul 3, 2024 · Conclusion Conclusion OpenVINO Model Server (OVMS) streamlines the deployment and management of deep learning models across various environments by leveraging the powerful optimization capabilities of the OpenVINO toolkit. Students in fields such as AI, machine learning, deep learning, natural language processing, or computer vision can also benefit, as they gain valuable experience in model optimization. Freeze the TensorFlow model if your model is not already frozen or skip this step and use the instruction to a convert a non-frozen model. Model optimiser requires some parameters in input, like the _input shape. In this example TensorFlow model ResNet will be used. Its Training-time Optimization 是一套用于 PyTorch 和 TensorFlow 2. Verification: Test the IR model using OpenVINO’s Inference Engine to ensure it’s performing as expected. Model Size: The IR format is lighter than ONNX or native framework formats Jan 20, 2021 · So, from my experience, now I should reinstall Ubuntu (just reinstall OpenVINO doesn't work) again and never use configuration for tensorflow for Model Optimizer - it ruins all optimizations. 14. jpg 2019-01-10 01:16:30. py - Entry point particularly for TensorFlow The following sections provide the information about how to use the Model Optimizer, from configuring the tool and generating an IR for a given model to customizing the tool for your needs: Configuring Model Optimizer Converting a Model to Intermediate Representation Feb 22, 2020 · The Model Optimizer 允許你使用非 OpenVINO Pre-Trained 好的model. This particular notebook shows the process where we perform the inference step on the freshly trained model that is converted to OpenVINO IR with model conversion API. 185 Operating System / Platform => Windows 10 Compiler => Problem classification => Model Inference Framework: Tensorflow Object Detection API 2. This step can be done using the so-called model optimiser as shown below. 1. How did you freeze? or maybe the model was not frozen; if not could you try again after freezing? Model Optimizer version: 2020. OpenVINO conversion API supports next model formats: PyTorch, TensorFlow, TensorFlow Lite, ONNX, and PaddlePaddle. May 13, 2024 · 介绍 OpenVINO Model Optimizer 模块，以及如何将TensorFlow pb 模型转化为 IR 模型（`mo --input_model . - Convert the PyTorch model to OpenVINO IR. weights --class_names coco. May 5, 2021 · Models need to be converted into a representation that the Inference Engine can load into devices for inference. The basic quantization flow is based on the following steps: Set up an environment and install dependencies. Apr 6, 2022 · Starting from the 2022. I try to convert a frozen_inference_graph. Quantization - OpenVINO supports post-training quantization (PTQ) to reduce model size and improve inference speed by converting floating-point weights to lower precision (e. - Download and prepare a dataset. xml (configuration file) and . When you run a pre-trained model through the Model Optimizer, it outputs an Intermediate OpenVINO is an open-source toolkit for deploying performant AI solutions in the cloud, on-prem, and on the edge alike. - Train your own model. - Compare performance of the FP32 and quantized models. pb for TensorFlow, . Oct 24, 2020 · I want to use the Openvino_2021 model_optimizer with a . 0-60-g0bc66e26ff [ FRAMEWORK ERROR ] Cannot load input model: TensorFlow cannot read the model file: "C:\Program Files (x86)\Intel\openvino_2020. For a TensorFlow model, the layout is [N,C,H,W]. We Jan 24, 2023 · I saved a tensorflow Deeplab segmentation model in the SavedModel format and converted it to IRv11 using openvino-dev model optimizer utility. Hence, the converted OpenVINO™ toolkit is an open source toolkit that accelerates AI inference with lower latency and higher throughput while maintaining accuracy, reducing model footprint, and optimizing hardware use. Training-time Optimization, a suite of advanced methods for training-time model optimization within the DL framework, such as PyTorch and TensorFlow 2. It provides the following tools: Model conversion API Benchmark Tool Accuracy Checker and Annotation Converter Post-Training Optimization Tool Model Downloader and other Open Model Zoo tools The instructions on this page show Convert TensorFlow Models to Accept Binary Inputs # This guide shows how to convert TensorFlow models and deploy them with the OpenVINO Model Server. To use it, you need a pre-trained deep learning model in one of the supported formats: TensorFlow, PyTorch, PaddlePaddle, MXNet, Caffe, Kaldi, or The conversion process is illustrated on the following image: Model Source: Begin with a model developed in a framework such as ONNX, Caffe, or one of the TensorFlow formats. It also explains how to scale the input tensors and adjust to use binary JPEG or PNG input data. 2. - Validate the converted model. Jan 6, 2021 · First and foremost, please take note that only these OS are supported by OpenVINO: Ubuntu 18. As instructed in the OpenVINO PyPI installation, you'll need to create virtual env (to avoid any conflict with your main host/system) and pip install OpenVINO in there. Converting a TensorFlow Model # This page provides general instructions on how to run model conversion from a TensorFlow format to the OpenVINO IR format. In OpenVINO, the default optimization tool is NNCF (Neural Network Compression Framework). It is an optional step, typically used only at the development stage, so that a pre-optimized model is used in the final AI application. pb model using Keras and tensorflow (version 1. This removal is done in two steps: Unimportant filters are zeroed out by the NNCF optimization with fine-tuning. - Live demo Model optimization means altering the model itself to improve its performance and reduce its size. When i ran inference on the IR model, the throughput that it gave was 5 FPS. pb --input - Validate the original model. Directly integrate models built with A summary of the steps for optimizing and deploying a model that was trained with the TensorFlow* framework: Configure the Model Optimizer for TensorFlow* (TensorFlow was used to train your model). caffemodel for Caffe, . Convert, optimize, and run inference utilizing the full potential of Intel® hardware. 0, 64-bit (for target only and requires modifications) Windows 10 Raspbian* Buster, 32-bit Raspbian* Stretch, 32-bit MacOS Based on your OS, make sure that you had setup OpenVINO correctly and you are able to run Jun 13, 2022 · Then, use Model Optimizer to convert the quantized model to IR format to run it with the OpenVINO runtime. After that, I saved the model as . Model optimization means altering the model itself to improve its performance and reduce its size. Jan 16, 2020 · What is the model optimizer and how to use it? In the previous part, we explained what OpenVINO toolkit is and its three main components: the model zoo, model optimizer, and inference engine. OpenVINO offers three optimization paths implemented in Neural Network Compression Framework (NNCF): Post-training Quantization is designed to optimize the inference of deep learning models by Model Optimizer Developer Guide Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. x long-term support (LTS), 64-bit CentOS 7. - Validate the original model. Prepare a representative calibration dataset that is Model Optimizer tries to deduce the framework by the extension of input model file (. 要使用该优化器，您需要采用以下支持格式之一的预训练深度学习模型：TensorFlow、PyTorch、PaddlePaddle、MXNet、Caffe、Kaldi 或 ONNX。 模型优化器将模型转换为 OpenVINO™ 中间表示 (IR) 格式，您之后可以通过 OpenVINO™ 运行时 对其进行推理。 The conversion process is illustrated on the following image: Model Source: Begin with a model developed in a framework such as ONNX, Caffe, or one of the TensorFlow formats. OpenVINO FAQs 1. You can check the currently supported TensorFlow operation set on this OpenVINO page. NNCF-optimized models can be inferred with OpenVINO using all the available workflows. pb" is incorrect TensorFlow model file. g. x. h5) format; (2) Converts the model from Keras into Tensorflow’s protobuf binary (. Model optimization (optional): Fine-tune the conversion process with flags for batch size, precision, and other model-specific optimizations. Model optimization is an optional offline step of improving the final model performance and reducing the model size by applying special optimization methods, such as 8-bit quantization, pruning, etc. mo --input_model model. pb --input_shape [1,3,130,206] --reverse_input_channels Regards, Aznie Sep 18, 2020 · Solved: With the release of Tensorflow 2 Object Detection, the Tensorflow team have uploaded a new model zoo to go with their new API. Same issues as Katsuya-san found. 如下圖: 首先你先訓練一個模型 (如 Tensorflow)，然後 將模型丟在 "Model Optimizer" 裡面傳換成 This tutorial demonstrates how to train, convert, and deploy an image classification model with TensorFlow and OpenVINO. pb`）。 _如何把tensorflow模型转为ir Model Optimizer is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. You must configure the Model Optimizer for the framework that was used to train the model. pb --input_shape "[1,224,224,3 如果使用模型优化器工具，则假定您已经有一个深度学习模型，该模型已使用其中一个支持框架（TensorFlow、PyTorch、PaddlePaddle、MXNet、Caffe、Kaldi）进行了训练，或以 ONNX* 格式表示。 模型优化器生成模型的中间表示 (IR)，可使用 OpenVINO™ 运行时 对其进行推理。 The left model is the original model, and the one on the right (after conversion) is the resulting model that the Model Optimizer produces, with BatchNorm and ScaleShift layers fused into the convolution weights rather than constituting separate layers. A training pipeline set up in the original framework (PyTorch or TensorFlow). This method returns a so-called compression controller and a wrapped model that can be used the same way as the original model. Jul 23, 2025 · Key Features of OpenVINO on Windows Model Optimizer: A cross-platform command-line tool that transforms TensorFlow, ONNX, and other frameworks’ models into an intermediate representation (IR) suitable for use in inference engines. This product delivers OpenVINO™ inline optimizations which enhance inferencing performance with minimal code modifications. params for Apache MXNet). May 7, 2023 · Hi All, I cannot successfully convert my tensorflow model in saved_model format My model directory is as follows： mo version: mo --version Version of Basic Quantization Flow # Introduction # The basic quantization flow is the simplest way to apply 8-bit quantization to the model. This command achieves the YOLOv4-tiny model conversion in three steps: (1) Converts the model weights from Darknet into Keras (. To optimize your model, you will need: A PyTorch or TensorFlow floating-point model. There are 2 kinds of models. The ssd_mobilenet_v2 model that you shared is a Dynamic Shaped model. Apply Optimization Methods # Wrap the original model object with the create_compressed_model() API using the configuration defined in the previous step. Oct 28, 2019 · OpenVino model optimizer error (FusedBatchNormV3) Asked 5 years, 6 months ago Modified 5 years, 3 months ago Viewed 1k times Model optimization means altering the model itself to improve its performance and reduce its size. 117\deployment_tools\model_optimizer\saved_model. After that to convert I tried too. 4" Apr 10, 2025 · More information in Release Policy. proto does not contain field" mean? Internally, the Model Optimizer uses a protobuf library to parse and load Caffe* models. 2. Here is the explanation. Model Optimizer: Use the Model Optimizer to convert the model into OpenVINO's Intermediate Representation (IR), resulting in . Jan 9, 2019 · I run again, Different error: (tensorflow) E:\AI\Data\OpenVINO>python tensorflow-yolo-v3/demo. Refer to Using Shape Inference for more information on how to deep-learning compiler inference docker-images tensorflow-serving tensorrt inference-engine openvino model-optimizer Updated on Dec 26, 2023 C++ To quickly start using OpenVINO™ Model Server follow these steps: Prepare Docker Download the OpenVINO™ Model server Provide a model Start the Model Server Container Prepare the Example Client Components Download data for inference Run inference Review the results Prerequisites # Model preparation: Python 3. Therefore, the ovc will exist only in that virtual environment. 0 Model name: Efficientdet D0 input resolution 512x512 trained with custom data and exported with Tensorflow object Apr 2, 2021 · Although have already installed OpenVINO™ (for instructions, see Related Links that follow)), you must install additional dependencies to enable the Model Optimizer tool within OpenVINO™ . - Download from GitHub, Caffe Zoo, TensorFlow* Zoo, and other resources. bin (weights file). - Other optimization possibilities with OpenVINO api - Live demo Table of contents: Get PyTorch model Prerequisites Instantiate model Convert model to OpenVINO IR Verify Jul 23, 2025 · Output: Predicted class of sample image The model outputs a vector of class scores, and the predicted class ID is the index with the highest score. The --input_shape argument value must base on the order of dimensions and it depends on the framework input layout of a model. The results are finally assembled to provide the final inference Dec 26, 2023 · This creates . pb file that i created with Tensorflow 2. Flexible Model Support: Use models trained with popular frameworks such as PyTorch, TensorFlow, ONNX, Keras, PaddlePaddle, and JAX/Flax. Jun 18, 2019 · Hello, I generated a . The main purpose of Model Optimizer is to convert the model to an IR. Jul 23, 2020 · Model Optimizer is an important component of the OpenVINO toolkit. py --weights_file yolov3-coco. jpg --output_img . I checked the OpenVino Toolkit documentation and found this (02/03/2020) Under 'Supported Topologies' there is a list with the topologies that work with the OpenVino model optimizer. python3 openvino/model-optimizer/mo_tf. I run the following command: sudo python3 Model Optimizer Frequently Asked Questions If your question is not covered by the topics below, use the OpenVINO™ Support page, where you can participate on a free forum. With these options, it is not necessary to normalize input data before propagating it through the network. These model formats can be read, compiled, and converted to OpenVINO IR, either automatically or explicitly. I use this Github "Tony607/object_detection_demo" with colab to learn how to convert a Tensorflow Graph with Openvino. To do it, you will need Model Optimizer, which is a command-line tool from the Developer Package of OpenVINO™ Toolkit. py --input_model=froz Converting TensorFlow* Object Detection API Models NOTES: * Starting with the 2021. With its support for popular frameworks like TensorFlow, PyTorch, Caffe, and ONNX, as well as multiple model formats, OVMS offers flexibility and ease of integration. 9 or higher with pip Model Server deployment: Installed Docker Engine or OVMS Filter Pruning of Convolutional Models # Introduction # Filter pruning is an advanced optimization method that allows reducing the computational complexity of the model by removing redundant or unimportant filters from the convolutional operations of the model. OpenVINO™ integration Nov 1, 2018 · Solved: Hi, I'm trying transform tensorflow model to OpenVINO IR files. Tensorflow deeplab Model zoo ( deeplab ) Fast Inference Optimization: Boost deep learning performance in computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks. |-- mo_tf. Known Issues and Limitations in the Model Optimizer Model Optimizer for TensorFlow* should be run on Intel® hardware that supports the AVX instruction set TensorFlow* provides only prebuilt binaries with AVX instructions enabled. Inference Optimization: Boost deep learning performance in computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks. Models that have been Feb 22, 2022 · TensorFlow models are directly supported by Model Optimizer, so the next step is using the following command in the terminal: mo --input_model v3-small_224_1. Nov 30, 2023 · Author: Ryan Loney, Product Manager, OpenVINO™ Toolkit Executive Summary It is simple to import PyTorch and TensorFlow models into OpenVINO with only a few lines of code. This mechanism is a core part of Model Optimizer, as a huge set of examples showing how to add custom logic to support your model. . Nov 27, 2024 · Follow the Model Optimizer to OpenVINO Model Converter transition guide for smoother transition. 1. I get a error when running the install_prerequisites_tf2. Develop your applications with both generative and conventional AI models, coming from the most popular model frameworks. TensorFlow models can be obtained from Kaggle or Hugging Face. 6, Tensorflow v1. Apr 11, 2023 · Hi Bbberk, Thanks for reaching out. 379. sh file. Figure 1 How does it really work under the hood? OpenVINO™ integration with TensorFlow* provides accelerated TensorFlow performance by efficiently partitioning TensorFlow graphs into multiple subgraphs, which are then dispatched to either the TensorFlow runtime or the OpenVINO™ runtime for optimal accelerated inferencing. Model Optimizer is no longer available. 0 as background. X. Oct 17, 2022 · The OpenVINO toolkit (Open Visual Inference and Neural network Optimization) is an open-source toolkit facilitating the optimization of a deep learning model from a framework and deployment using an inference engine onto Intel hardware like CPUs. Nov 26, 2019 · Hello I'm on Windows 10 with Python v3. xml and . The diagram below shows two common use cases and workflows where POT and NNCF are utilized. 906151: I d:\build\tensorflow\tensorflow-r1. Tools: The OpenVINO™ Development Tools package (pip install openvino-dev) is no longer available for OpenVINO releases in 2025. The EfficientDet-D0 model was downloaded and converted to IR format using the following command: python mo_tf. X or TensorFlow v2. To run the network with OpenVINO™ Toolkit, you need firstly convert it to Intermediate Representation (IR). pb file as subfolders init. The file should contain one of the following TensorFlow graphs: Once the model is optimized, you may convert it to the OpenVINO IR format, getting even better inference results with OpenVINO Runtime. Oct 7, 2022 · In this article, we showcase OpenVINO™ Model Optimizer to optimize the Big Transfer (BiT) model and compare inference performance with TensorFlow and OpenVINO™ on Intel® edge hardware. The models are saved to the current directory. x 等深度学习框架内训练时间模型优化的高级方法。 它支持量化感知训练和过滤器修剪等方法。 经过 NNCF 优化的模型可以利用所有可用工作流程使用 OpenVINO 进行推理。 Aug 6, 2021 · Hello Milos Acimovic, We reproduced your issue, and were able to convert the TensorFlow 2 EfficientDet-D0 model and subsequently executed Object Detection Python Demo and also Object Detection Python Sample SSD using the converted model with OpenVINO 2021. Try using the command below to convert your model. Apr 23, 2019 · Hi I have been trying for days now to convert a tensorflow graph to use with Neural compute stick2. It is available for models in the following frameworks: OpenVINO, PyTorch, TensorFlow 2. I get following error. 4. If you want to know how to use the newer OpenVINO API please check this notebook. , INT8). This guide uses the Model Downloader to get pre-trained models. ckpt file having assets, variables, . TensorFlow model can be converted into Intermediate Representation format using model_optimizer Model Optimizer extensibility mechanism enables support of new operations and custom transformations to generate the optimized intermediate representation (IR) as described here. Zero filters are Aug 11, 2021 · I am getting shape inference error when I try to run openvino model optimizer on a simple custom layer model. py --saved_model_dir model/ --input_shape=[1 Troubleshooting Guide for OpenVINO™ Installation & Configuration Use Case - Integrate and Save Preprocessing Steps Into IR Predict on Binary Inputs via TensorFlow Serving API Dynamic batch size with OpenVINO™ Model Server Demultiplexer Demonstrating integration of Open WebUI with OpenVINO Model Server How to serve LLM models with Continuous Batching via OpenAI API Scaling on a dual CPU Convert a TensorFlow Model to OpenVINO IR Format ¶ Use Model Optimizer to convert a TensorFlow model to OpenVINO IR with FP16 precision. pb) format; (3) Converts the model from Tensorflow into IR format with FP32 and FP16 precisions using Model Optimizer. OpenVINO conversion API supports next model formats: PyTorch, TensorFlow, TensorFlowLite, ONNX, and PaddlePaddle. Here is unit test: import os import shutil import pytest Jun 7, 2019 · Overview This example demonstrates the use of Deep Learning APIs to perform Object Detection using both TensorFlow and OpenVINO Optimized Models. The TensorFlow model is converted to OpenVINO model using Model Optimizer. Known limitations are TensorFlow model with TF1 Control flow and object detection models. For more details, see the model conversion transition guide. I'm able to do inference on Install OpenVINO™ Development Tools ¶ OpenVINO Development Tools is a set of utilities that make it easy to develop and optimize models and applications for OpenVINO. py I trained a custom CNN model using keras and tensorflow 2. Note: This article was created with OpenVINO 2022. You cannot perform inference on your trained model without having first run the model through the Model Optimizer. 04. The Mar 3, 2024 · Model Optimization Techniques (Pruning, Quantization, Knowledge Distillation, Sparsity, OpenVino Toolkit)💡 Model optimization in deep learning refers to the process of improving the performance … Model Optimizer extensibility mechanism enables support of new operations and custom transformations to generate the optimized intermediate representation (IR) as described here. names --input_img pupils. 1 release the Model Optimizer can generate an IR with partially defined input shapes ("-1" dimension in the TensorFlow model or dimension with string value in the ONNX model). 0_float. Your input model might have a different extension and you need to explicitly set the source framework. Jan 29, 2019 · I received when trying to convert a frozen model on the machine running openVino (tensorflow 1. 9\tensorflow\core\platform\cpu_feature_guard. As-is, these Options to find a model suitable for the OpenVINO™ toolkit are: - Download public and Intel's pre-trained models from the Open Model Zoo using the Model Downloader tool. Training and validation datasets. - Prepare and run optimization pipeline. 11. What is the OpenVINO toolkit used for? Jan 23, 2023 · OpenVINO™ integration with TensorFlowOpenVINO™ integration with TensorFlow OpenVINO™ integration with TensorFlow is a product designed for TensorFlow* developers who want to get started with OpenVINO™ in their inferencing applications. 6, 64-bit (for target only) Yocto Project v3. The easiest way to get it is Model Conversion - The model is converted from its original framework (TensorFlow, PyTorch, or Keras) into OpenVINO's IR format using the Model Optimizer. When you create a model using TensorFlow for Poets 2, you need make sure you choose an architecture that is supported by the model optimizer. - Compare accuracy of the FP32 and quantized models. /out. pb with this commande : python mo_tf. This section tells you how to configure the Model Optimizer either through scripts or by using a manual process. cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled Aug 12, 2021 · System information (version) OpenVINO=> For conversion OpenVino in Github c8af311 from 2021-06-11 and for inference openvino_2021. I even re-created a VM to use the latest version Aug 2, 2021 · Further down, we will also show you how to use the Model Optimizer, and the way models are converted from Caffe and TensorFlow frameworks to the OpenVINO IR format. Time and Space Considerations Inference Time: OpenVINO significantly reduces latency compared to standard PyTorch/TensorFlow runtimes, especially on Intel hardware. Jan 28, 2019 · Solved: Hi, I'm trying to convert frozen tensorflow models to IR models using the OpenVINO model optimizer. The set of required dependencies varies depending on the framework (such as Caffe, TensorFlow, or TensorFlow 2). Prepare PyTorch model Convert PyTorch model to OpenVINO IR Run model inference with OpenVINO Prepare and run optimization pipeline Compare performance of the FP32 and quantized models. Consider using the new conversion methods instead. bin files (the IR). 14, openvino_2019. Add mean values to the model and scale the output with the standard deviation with --scale_values. Aug 30, 2024 · The ovc will exist according to where you installed the OpenVINO. It streamlines AI development and integration of deep learning in domains like computer vision, large language models (LLM), and generative AI. For more details, refer to Model Preparation documentation. May 9, 2022 · The Model Optimizer is a Python*-based command line tool for importing trained models from popular deep learning frameworks such as Caffe*, TensorFlow*, Apache MXNet*, ONNX* and Kaldi*. This library requires a file grammar and a Sep 29, 2020 · Hi , I'm trying to convert a keras (tensorflow) model to openvino format using model optimizer. There are three main tools in OpenVINO to meet all your deployment Jan 29, 2020 · Eventually I found a solution that worked for me. Run optimized model inference on video Table of contents: Prerequisites Get PyTorch model Convert PyTorch model to OpenVINO IR Verify model inference Preprocessing Model Optimizer version: 2021. 0) but trained on a machine running tensorflow 1. 9. [ WARNING ] Consider building the Inference Engine Python API from sources or reinstall OpenVINO (TM) toolkit using "pip install openvino==2021. Converting TensorFlow model Once the model is optimized, you may convert it to the OpenVINO IR format, getting even better inference results with OpenVINO Runtime. 2-3974-e2a469a3450-releases/2021/4 [ WARNING ] Model Optimizer and Inference Engine versions do no match. Directly integrate models built with Apr 12, 2023 · Hi Bbberk, Thanks for reaching out. What does the message " [ ERROR ]: Current caffe. 1 release, the Model Optimizer converts the TensorFlow* Object Detection API SSDs, Faster and Mask RCNNs topologies keeping shape-calculating sub-graphs by default, so topologies can be re-shaped in the Inference Engine using dedicated reshape API. Intel社が提供しているOpenVINOにおいて、学習済みモデルはIR (Intermediate Representation)という形式で推論を行うことができます。 TensorflowやCaffeなど他の学習済みモデルの形式の場合は、OpenVINOツールキットに入っているModel Optimizerを用いてIR形式に変換します。 3. bsb qfja uxggy ozer tad lvu idkrx kpy yexca kcghp kzrmvqs pcomr zpsapsy zik coard