python3 -m virtualenv venv
source venv/bin/activate
python -m pip install .

After sourcing the virtual environmnet

python -m torchtmpl.main config.yml train

With the sample configuration file, with a resnet18, you should get around 77% of validation accuracy after 100 epochs.

Once a model is trained, you can run the captum insights visualization tool.

The trained model is saved in the logs subdirectory. You need to provide the specific run you want to visualize. For example, for visualizing the run saved in logs/resnet18_0 :

python -m torchtmpl.visualize logs/resnet18_0/

That should start the flask application to which you can connect with your browser and then experiment with the visualization algorithms. An example is displayed below.