Those pre-trained models are implemented and trained on a particular deep learning framework/library such as TensorFlow, PyTorch, Caffe, etc. Here’s a summary of existing architectures (pre-trained models) You can see a list of the most common pre-trained models (such as Inception v3, Resnet v2101, Yolo, etc.) at the and in particular if focusing on computer vision (Image Classification and Object Detection) here: That kind of large scale training would require too much specialized resources for most developers or even most organizations. Those pre-trained models (also called ‘architectures’) are the culmination of many deep neural networks (DNN) architecture ideas developed by multiple researchers over the years and usually trained on very large datasets with many millions of images (such as the ImageNet dataset). train a custom modelīefore getting into the specific subject of this blog post focusing on “training a model”, I also want to highlight that in ML.NET you can also do the simplest thing which is to run/score an already pre-trained deep learning model to only run predictions.

However, for this blog post I am only focusing on Image Classification/Recognition and the multiple approaches you can take with ML.NET in order to train a Deep Learning model for image Classification/Recognition. Image Classification, Object Detection and Text Analysis are probably the most common tasks in Deep Learning which is a subset of Machine Learning. Context and background for ‘Image Classification’, ‘training vs. 6th 2019 so it covers the updates provided in ML.NET 1.4 GA, such as Image classifier training and inference using GPU and a simplified API. Note that this blog post was updated on Nov. Blog Post updated targeting ML.NET 1.4 GA (Nov.

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