You can find runnable examples in the examples directory. We are planning to add more examples for different tasks. If you have an idea or want to contribute or improve an example, please don't hesitate to open an issue or create a PR.

Running the examples

You can clone the repository and run the examples as part of the build, for instance:

sbt> examples/runMain LeNetApp

The examples are also scala-cli scripts so you can run them with scala-cli, either locally or directly from the repo:

scala-cli https://raw.githubusercontent.com/sbrunk/storch/main/examples/src/main/scala/ImageClassifier.scala

Image classifier example

Example script for training an image-classification model on your own images and running inference. It uses the ResNet model implementation.

It will also automatically download converted pre-trained weights from the releases. See converting pre-trained weights from PyTorch for details.


To train a new image classifier on your own images run:

scala-cli ImageClassifier.scala -- train --dataset-dir <dataset>

Where the expected dataset is a directory per class with examples, like this:

├── PetImages
    ├── Cat
    │   ├── 1.jpg
    │   ├── 2.jpg
    │   ├── ...
    └── Dog
        ├── 1.jpg
        ├── 2.jpg
        ├── ...

Using a smaller base model:

scala-cli ImageClassifier.scala -- train --base-model ResNet18 --dataset-dir <dataset>

To see all options, run:

scala-cli ImageClassifier.scala -- train -h

Training on the GPU

Right now, if you're using scala-cli you have to edit the directives at the top of the ImageClassifier.scala script to enable GPU support (see comments in the script). We're looking for ways to make this easier in the future.


Once you've trained a model, you can use it for predicitons:

scala-cli ImageClassifier.scala -- predict --image-path <some-image.jpg>

If you don't have your own images, you can use an example dataset, for instance, the Cat VS Dog dataset (alternative download) without requiring a kaggle account) is already in the right format.