Command Line Interface

After installing openretina, you can inspect all available subcommands with:

openretina --help

Create synthetic test data

# Show help
openretina create-data --help

# Create synthetic data under ./test_data
openretina create-data ./test_data --num-colors 3 --num-stimuli 4 --num-sessions 2

And use this artificial data to train a new model from scratch using the provided configs (make sure you have also cloned the Github repository for this). When working with your own dataset, be sure to adjust the number of colour channels and the video dimensions accordingly and specify the names of the stimuli to be used for testing.

Train a model

Train with local HDF5-style data

openretina train --config-path configs --config-name hdf5_core_readout \
  paths.data_dir="test_data" \
  data_io.test_names="[random_noise_2, random_noise_3]" \
  data_io.color_channels=3 \
  data_io.video_height=16 \
  data_io.video_width=8

Train with a built-in dataset config

openretina train --config-path configs --config-name hoefling_2024_core_readout_low_res

Evaluate a model

Use openretina eval to run the evaluation pipeline for one split (test by default):

openretina eval --config-path configs --config-name karamanlis_2024_eval \
  evaluation.model_path=karamanlis_2024_base_mouse

Where the model path can either be the model tag of one of the models stored in the openretina huggingface, or a path to a local checkpoint you have trained. Similarly, the config path and name can be set to the local configs you have used for training.

You can also evaluate on a different split by overriding:

openretina eval --config-path configs --config-name karamanlis_2024_eval \
  evaluation.model_path=karamanlis_2024_base_mouse \
  evaluation.data_split=validation

For split semantics and multi-test dataloader behavior, see Data IO flow and multi-test support.

Visualize model neurons

The model path can be either a local checkpoint path or a Hugging Face model identifier:

# Show visualization options
openretina visualize --help

# Download and visualize a pretrained model
openretina visualize --model-path hoefling_2024_base_low_res --save-folder visualizations

# Visualize original Hoefling et al. (2024) ensemble model
openretina visualize --is-hoefling-ensemble-model --model-id 0 --save-folder vis_ensemble_0