![]() ![]() We can make use of these internal image features in the model to train a new model with far fewer classes.Īs shown in the following diagram, you add a reference to the ML.NET NuGet packages in your. The TensorFlow model classifies entire images into a thousand classes, such as “Umbrella”, “Jersey”, and “Dishwasher”.īecause the Inception model has already been pre-trained on thousands of different images, internally it contains the image features needed for image identification. This tutorial uses the TensorFlow Inception deep learning model, a popular image recognition model trained on the ImageNet dataset. You can use the Inception model's ability to recognize and classify images to the new limited categories of your custom image classifier. The Inception model is trained to classify images into a thousand categories, but for this tutorial, you need to classify images in a smaller category set, and only those categories. This tutorial scales that process down even further, using only a dozen training images. ![]() millions of labeled images and build a customized model fairly quickly (within an hour on a machine without a GPU). While not as effective as training a custom model from scratch, using a pre-trained model allows you to shortcut this process by working with thousands of images vs. Training an image classification model from scratch requires setting millions of parameters, a ton of labeled training data and a vast amount of compute resources (hundreds of GPU hours).
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