# Get Started with OpenVINO™ Toolkit on Linux* This guide provides you with the information that will help you to start using the OpenVINO™ Toolkit on Linux\*. With this guide, you will learn how to: 1. [Configure the Model Optimizer](#configure-the-model-optimizer) 2. [Prepare a model for sample inference](#prepare-a-model-for-sample-inference) 1. [Download a pre-trained model](#download-a-trained-model) 2. [Convert the model to an Intermediate Representation (IR) with the Model Optimizer](#convert-the-model-to-an-intermediate-representation-with-the-model-optimizer) 3. [Run the Image Classification Sample Application with the model](#run-the-image-classification-sample-application) ## Prerequisites 1. This guide assumes that you have already cloned the `openvino` repo and successfully built the Inference Engine and Samples using the [build instructions](inference-engine/README.md). 2. The original structure of the repository directories remains unchanged. > **NOTE**: Below, the directory to which the `openvino` repository is cloned is referred to as ``. ## Configure the Model Optimizer 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\*. You cannot perform inference on your trained model without having first run the model through the Model Optimizer. When you run a pre-trained model through the Model Optimizer, it outputs an *Intermediate Representation*, or *(IR)* of the network, a pair of files that describes the whole model: - `.xml`: Describes the network topology - `.bin`: Contains the weights and biases binary data For more information about the Model Optimizer, refer to the [Model Optimizer Developer Guide]. ### Model Optimizer Configuration Steps You can choose to either configure all supported frameworks at once **OR** configure one framework at a time. Choose the option that best suits your needs. If you see error messages, check for any missing dependencies. > **NOTE**: The TensorFlow\* framework is not officially supported on CentOS\*, so the Model Optimizer for TensorFlow cannot be configured on, or run with CentOS. > **IMPORTANT**: Internet access is required to execute the following steps successfully. If you access the Internet via proxy server only, please make sure that it is configured in your OS environment as well. **Option 1: Configure all supported frameworks at the same time** 1. Go to the Model Optimizer prerequisites directory: ```sh cd /model_optimizer/install_prerequisites ``` 2. Run the script to configure the Model Optimizer for Caffe, TensorFlow, MXNet, Kaldi\*, and ONNX: ```sh sudo ./install_prerequisites.sh ``` **Option 2: Configure each framework separately** Configure individual frameworks separately **ONLY** if you did not select **Option 1** above. 1. Go to the Model Optimizer prerequisites directory: ```sh cd /model_optimizer/install_prerequisites ``` 2. Run the script for your model framework. You can run more than one script: - For **Caffe**: ```sh sudo ./install_prerequisites_caffe.sh ``` - For **TensorFlow**: ```sh sudo ./install_prerequisites_tf.sh ``` - For **MXNet**: ```sh sudo ./install_prerequisites_mxnet.sh ``` - For **ONNX**: ```sh sudo ./install_prerequisites_onnx.sh ``` - For **Kaldi**: ```sh sudo ./install_prerequisites_kaldi.sh ``` The Model Optimizer is configured for one or more frameworks. Continue to the next session to download and prepare a model for running a sample inference. ## Prepare a Model for Sample Inference This section describes how to get a pre-trained model for sample inference and how to prepare the optimized Intermediate Representation (IR) that Inference Inference Engine uses. ### Download a Trained Model To run the Image Classification Sample, you need a pre-trained model to run the inference on. This guide uses the public SqueezeNet 1.1 Caffe\* model. You can find and download this model manually or use the OpenVINO™ [Model Downloader]. With the Model Downloader, you can download other popular public deep learning topologies and [OpenVINO™ pre-trained models], which are already prepared for running inference upon a wide list of inference scenarios: * object detection, * object recognition, * object re-identification, * human pose estimation, * action recognition, and others. To download the SqueezeNet 1.1 Caffe* model to a `models` folder (referred to as `` below) with the Model Downloader: 1. Install the [prerequisites]. 2. Run the `downloader.py` script, specifying the topology name and the path to your ``. For example, to download the model to a directory named `~/public_models`, run: ```sh ./downloader.py --name squeezenet1.1 --output_dir ~/public_models ``` When the model files are successfully downloaded, output similar to the following is printed: ```sh ###############|| Downloading topologies ||############### ========= Downloading /home/username/public_models/classification/squeezenet/1.1/caffe/squeezenet1.1.prototxt ========= Downloading /home/username/public_models/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel ... 100%, 4834 KB, 3157 KB/s, 1 seconds passed ###############|| Post processing ||############### ========= Changing input dimensions in squeezenet1.1.prototxt ========= ``` ### Convert the model to an Intermediate Representation with the Model Optimizer > **NOTE**: This section assumes that you have configured the Model Optimizer using the instructions from the [Configure the Model Optimizer](#configure-the-model-optimizer) section. 1. Create a `` directory that will contains the Intermediate Representation (IR) of the model. 2. Inference Engine can perform inference on a [list of supported devices] using specific device plugins. Different plugins support models of [different precision formats], such as `FP32`, `FP16`, `INT8`. To prepare an IR to run inference on particular hardware, run the Model Optimizer with the appropriate `--data_type` options: **For CPU (FP32):** ```sh python3 /model_optimizer/mo.py --input_model /classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP32 --output_dir ``` **For GPU and MYRIAD (FP16):** ```sh python3 /model_optimizer/mo.py --input_model /classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP16 --output_dir ``` After the Model Optimizer script is completed, the produced IR files (`squeezenet1.1.xml`, `squeezenet1.1.bin`) are in the specified `` directory. 3. Copy the `squeezenet1.1.labels` file from the `/scripts/demo/` folder to the model IR directory. This file contains the classes that ImageNet uses so that the inference results show text instead of classification numbers: ```sh cp /scripts/demo/squeezenet1.1.labels ``` Now you are ready to run the Image Classification Sample Application. ## Run the Image Classification Sample Application The Inference Engine sample applications are automatically compiled when you built the Inference Engine using the [build instructions](inference-engine/README.md). The binary files are located in the `/inference-engine/bin/intel64/Release` directory. To run the Image Classification sample application with an input image on the prepared IR: 1. Go to the samples build directory: ```sh cd /inference-engine/bin/intel64/Release 2. Run the sample executable with specifying the `car.png` file from the `/scripts/demo/` directory as an input image, the IR of your model and a plugin for a hardware device to perform inference on: **For CPU:** ```sh ./classification_sample -i /scripts/demo/car.png -m /squeezenet1.1.xml -d CPU ``` **For GPU:** ```sh ./classification_sample -i /scripts/demo/car.png -m /squeezenet1.1.xml -d GPU ``` **For MYRIAD:** >**NOTE**: Running inference on VPU devices (Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires performing [additional hardware configuration steps](inference-engine/README.md#optional-additional-installation-steps-for-the-intel-movidius-neural-compute-stick-and-neural-compute-stick-2). ```sh ./classification_sample -i /scripts/demo/car.png -m /squeezenet1.1.xml -d MYRIAD ``` When the Sample Application completes, you will have the label and confidence for the top-10 categories printed on the screen. Below is a sample output with inference results on CPU: ```sh Top 10 results: Image /home/user/openvino/scripts/demo/car.png classid probability label ------- ----------- ----- 817 0.8363345 sports car, sport car 511 0.0946488 convertible 479 0.0419131 car wheel 751 0.0091071 racer, race car, racing car 436 0.0068161 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 656 0.0037564 minivan 586 0.0025741 half track 717 0.0016069 pickup, pickup truck 864 0.0012027 tow truck, tow car, wrecker 581 0.0005882 grille, radiator grille total inference time: 2.6642941 Average running time of one iteration: 2.6642941 ms Throughput: 375.3339402 FPS [ INFO ] Execution successful ``` ## Additional Resources * [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes) * [Inference Engine build instructions](inference-engine/README.md) * [Introduction to Intel® Deep Learning Deployment Toolkit](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Introduction.html) * [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html) * [Model Optimizer Developer Guide] * [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html). [Model Optimizer Developer Guide]:https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html [Model Downloader]:https://github.com/opencv/open_model_zoo/tree/master/tools/downloader [OpenVINO™ pre-trained models]:https://github.com/opencv/open_model_zoo/tree/master/models/intel [prerequisites]:https://github.com/opencv/open_model_zoo/tree/master/tools/downloader#prerequisites [list of supported devices]:https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_Supported_Devices.html [different precision formats]:https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_Supported_Devices.html#supported_model_formats