DenseNet

This article describes how to use the DenseNet module in Azure Machine Learning designer (preview), to create an image classification model using the Densenet algorithm.

This classification algorithm is a supervised learning method, and requires a labeled dataset. Refer to Convert to Image Directory module for more instruction about how to get a labeled image directory. You can train the model by providing the model and the labeled image directory as inputs to Train Pytorch Model. The trained model can then be used to predict values for the new input examples using Score Image Model.

More about DenseNet

Refer to Densely Connected Convolutional Networks for more details.

How to configure DenseNet

  1. Add the DenseNet module to your pipeline in the designer.

  2. For Model name, specify name of a certain densenet structure and you can select from supported densenet: 'densenet121', 'densenet161', 'densenet169', 'densenet201'.

  3. For Pretrained, specify whether to use a model pre-trained on ImageNet. If selected, you can fine tune model based on selected pre-trained model; if deselected, you can train from scratch.

  4. For Memory efficient, specify whether to use checkpointing, which is much more memory-efficient but slower. See https://arxiv.org/pdf/1707.06990.pdf for more information.

  5. Connect the output of DenseNet module, training and validation image dataset module to the Train Pytorch Model.

  6. Submit the pipeline.

Results

After pipeline run is completed, to use the model for scoring, connect the Train Pytorch Model to Score Image Model, to predict values for new input examples.

Technical notes

Module parameters

Name Range Type Default Description
Model name Any Mode densenet201 Name of a certain densenet structure
Pretrained Any Boolean True Whether to use a model pre-trained on ImageNet
Memory efficient Any Boolean False Whether to use checkpointing, which is much more memory efficient but slower

Output

Name Type Description
Untrained model UntrainedModelDirectory An untrained densenet model that can be connected to Train Pytorch Model.

Next steps

See the set of modules available to Azure Machine Learning.