Test and retrain a model with Custom Vision Service

After you train your model, you can quickly test it using a locally stored image or an online image. The test uses the most recently trained iteration.

Test your model

  1. From the Custom Vision web page, select your project. Select Quick Test on the right of the top menu bar. This action opens a window labeled Quick Test.

    The Quick Test button is shown in the upper right corner of the window.

  2. In the Quick Test window, click in the Submit Image field and enter the URL of the image you want to use for your test. If you want to use a locally stored image instead, click the Browse local files button and select a local image file.

    Image of the submit image page

The image you select appears in the middle of the page. Then the results appear below the image in the form of a table with two columns, labeled Tags and Confidence. After you view the results, you may close the Quick Test window.

You can now add this test image to your model and then retrain your model.

Use the predicted image for training

To use the image submitted previously for training, use the following steps:

  1. To view images submitted to the classifier, open the Custom Vision web page and select the Predictions tab.

    Image of the predictions tab

    Tip

    The default view shows images from the current iteration. You can use the Iteration drop down field to view images submitted during previous iterations.

  2. Hover over an image to see the tags that were predicted by the classifier.

    Tip

    Images are ranked, so that the images that can bring the most gains to the classifier are at the top. To select a different sorting, use the Sort section.

    To add an image to your training data, select the image, select the tag, and then select Save and close. The image is removed from Predictions and added to the training images. You can view it by selecting the Training Images tab.

    Image of the tagging page

  3. Use the Train button to retrain the classifier.

Next steps

Improve your classifier