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Var scissorsTag = trainingApi.CreateTag(project.Id, "scissors") įirst, download the sample images for this project. Var forkTag = trainingApi.CreateTag(project.Id, "fork") private void AddTags(CustomVisionTrainingClient trainingApi, Project project) This method defines the tags that you will train the model on. Project = trainingApi.CreateProject("My New Project", null, objDetectionDomain.Id) Var objDetectionDomain = domains.FirstOrDefault(d => d.Type = "ObjectDetection") Ĭonsole.WriteLine("Creating new project:") private Project CreateProject(CustomVisionTrainingClient trainingApi) See the CreateProject method to specify other options when you create your project (explained in the Build a detector web portal guide). The created project will show up on the Custom Vision website. This next method creates an object detection project. Create a prediction endpoint, passing in the obtained prediction keyĬustomVisionPredictionClient predictionApi = new CustomVisionPredictionClient(new .(predictionKey)) Private CustomVisionPredictionClient AuthenticatePrediction(string endpoint, string predictionKey) Create the Api, passing in the training keyĬustomVisionTrainingClient trainingApi = new CustomVisionTrainingClient(new .(trainingKey)) private CustomVisionTrainingClient AuthenticateTraining(string endpoint, string trainingKey, string predictionKey) In a new method, instantiate training and prediction clients using your endpoint and keys.
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These code snippets show you how to do the following tasks with the Custom Vision client library for. It includes properties for the object ID and name, the bounding box location of the object, and a confidence score. This class defines a single object prediction on a single image. This class handles the querying of your models for object detection predictions. This class handles the creation, training, and publishing of your models. Project project = CreateProject(trainingApi) CustomVisionTrainingClient TrainingApi = AuthenticateTraining(trainingEndpoint, trainingKey) ĬustomVisionPredictionClient predictionApi = AuthenticatePrediction(predictionEndpoint, predictionKey) In the application's Main method, add calls for the methods used in this quickstart. See the Cognitive Services security article for more information. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. Remember to remove the keys from your code when you're done, and never post them publicly. NET with the following command: dotnet add package .CustomVision.Training -version 2.0.0ĭotnet add package .CustomVision.Prediction -version 2.0.0
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Within the application directory, install the Custom Vision client library for. The build output should contain no warnings or errors. You can build the application with: dotnet build dotnet new console -n custom-vision-quickstartĬhange your directory to the newly created app folder. This command creates a simple "Hello World" C# project with a single source file: program.cs. In a console window (such as cmd, PowerShell, or Bash), use the dotnet new command to create a new console app with the name custom-vision-quickstart. Select the latest version and then Install. In the package manager that opens select Browse, check Include prerelease, and search for .CustomVision.Training and .CustomVision.Prediction.
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Once you've created a new project, install the client library by right-clicking on the project solution in the Solution Explorer and selecting Manage NuGet Packages.
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Azure subscription - Create one for free.Reference documentation | Library source code (training) (prediction) | Package (NuGet) (training) (prediction) | Samples Prerequisites Use the Custom Vision client library for. If you want to build and train an object detection model without writing code, see the browser-based guidance instead.