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fixed link to Intel models and model downloader

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Alexey Suhov 2019-11-15 13:55:44 +03:00
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get-started-linux.md
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@ -87,12 +87,12 @@ This paragraph contains the steps to get the pre-trained model for sample infere
### Download a Trained Model
To run the Image Classification Sample you'll need a pre-trained model to run the inference on. This guide will use the public SqueezeNet 1.1 Caffe* model. You can find and download this model manually or use the OpenVINO™ [Model Downloader](https://github.com/opencv/open_model_zoo/tree/master/model_downloader).
To run the Image Classification Sample you'll need a pre-trained model to run the inference on. This guide will use the public SqueezeNet 1.1 Caffe* model. You can find and download this model manually or use the OpenVINO™ [Model Downloader](https://github.com/opencv/open_model_zoo/tree/master/tools/downloader).
With the Model Downloader, you can download other popular public deep learning topologies and the [OpenVINO™ pre-trained models](https://github.com/opencv/open_model_zoo/tree/master/intel_models) prepared for running inference for a wide list of inference scenarios: object detection, object recognition, object re-identification, human pose estimation, action recognition and others.
With the Model Downloader, you can download other popular public deep learning topologies and the [OpenVINO™ pre-trained models](https://github.com/opencv/open_model_zoo/tree/master/models/intel) prepared for running inference for 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 with the Model Downloader:
1. Install the [prerequisites](https://github.com/opencv/open_model_zoo/tree/master/model_downloader#prerequisites).
1. Install the [prerequisites](https://github.com/opencv/open_model_zoo/tree/master/tools/downloader#prerequisites).
2. Run the `downloader.py` with specifying the topology name and a `<models_dir>` path. For example to download the model to the `~/public_models` directory:
```sh
./downloader.py --name squeezenet1.1 --output_dir ~/public_models