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  # SqueezeNet Object Detection sample
  
  This UWP application uses SqueezeNet, a pre-trained machine learning model, to detect the predominant object in an image selected by the user from a file.
  
  This sample demonstrates the use of the [Windows.AI.MachineLearning](https://docs.microsoft.com/uwp/api/windows.ai.machinelearning) API to load a model, bind an input image and an output tensor, and evaluate a binding.
  
  If you would like to use a different model, then you can use [Netron](https://github.com/lutzroeder/Netron) to determine the input and output requirements of your ONNX model.
  
  ## Prerequisites
  
  
  - [Visual Studio 2017 Version 15.7.4 or Newer](https://developer.microsoft.com/en-us/windows/downloads)
  - [Windows 10 - Build 17763 or higher](https://www.microsoft.com/en-us/software-download/windowsinsiderpreviewiso)
  - [Windows SDK - Build 17763 or higher](https://developer.microsoft.com/en-US/windows/downloads/windows-10-sdk)
  
  ## Build the sample
  
  1. If you download the samples ZIP, be sure to unzip the entire archive, not just the folder with
     the sample you want to build.
  
  2. Start Microsoft Visual Studio 2017 and select **File** \> **Open** \> **Project/Solution**.
  
  3. Starting in the folder where you unzipped the samples, go to the Samples subfolder, then the
     subfolder for this specific sample. Double-click the Visual Studio solution file (.sln) file.
  
  4. Change the platform target default to x64 or x86 if you want to test on a non-ARM device.
  
  5. Press Ctrl+Shift+B, or select **Build** \> **Build Solution**.
  
  ## Run the sample
  
  The next steps depend on whether you just want to deploy the sample or you want to both deploy and run it.
  
  ### Deploying the sample
  
  - Select Build > Deploy Solution.
  
  ### Deploying and running the sample
  
  - To debug the sample and then run it, press F5 or select Debug >  Start Debugging. To run the sample without debugging, press Ctrl+F5 or selectDebug > Start Without Debugging.
  
  ## License
  
  MIT. See [LICENSE file](https://github.com/Microsoft/Windows-Machine-Learning/blob/master/LICENSE).
  