With ML.NET and related NuGet packages for TensorFlow you can currently do the following: Here's a Getting started sample on scoring a TensorFlow model which is using the Inception pre-trained TensorFlow model. Transfer Learning on top of a pre-trained TensorFlow model: You can re-use part of an already pre-trained TensorFlow model (such as the Inception pre-trained TensorFlow model) to build a new model trained with additional samples for the final layer, such as trained with new images. For instance, see this Tutorial on how to use Transfer Learning with ML.NET by using an already trained Image Classifier TensorFlow model to build a new custom model to classify images into different categories. However, in the scenario where you want to train with your own images, the Transfer Learning approach can be a bit complex because even without taking into account the code implementation for transfer learning you'll need to find a base TensorFlow model to train on top of it which was originally trained with similar image types to your new images. Here's some specific examples to understand that statement: For instance, the TensorFlow Inception model was trained with photos of may objects, animals, vegetables and people, so you could train the final layer, let's say with photos of'super heroes', and the model will clasify properly images of specific'super heroes'.