Get Pretrained Models
Instead of creating a deep learning model from scratch, get a pretrained model, which you can apply directly or adapt to your task.
Explore MATLAB® Deep Learning Model Hub to access the latest models by category and get tips on choosing a model.
Load most models at the command line. For example:
net = darknet19;
Convert TensorFlow™, PyTorch®, and ONNX™ models to MATLAB networks by using an import function. For example:
net = importTensorFlowNetwork("EfficientNetV2L")
Apply Pretrained Models
Apply pretrained models to image classification, computer vision, audio processing, lidar processing, and other deep learning workflows.
- Find the right pretrained model and apply it directly to your task.
- Perform transfer learning by adapting a pretrained model to a new task or dataset. Updating and retraining a model is faster and easier than creating it from scratch.
- Use a pretrained model as a feature extractor by using the layer activations as features. Then use these features to train another machine learning model, such as a support vector machine (SVM).
- Use a pretrained model as the foundation for another type of model. For example, use a convolutional neural network as a starting point to create an object detection or a semantic segmentation model.
Tips to Select Models
There are many pretrained models to choose from, and each model has tradeoffs: