Neural Network#

SAClient.download_model(model, output_dir)#

Downloads the neural network and related files which are the <model_name>.pth/pkl. <model_name>.json, <model_name>.yaml, classes_mapper.json

  • model (dict) – the model that needs to be downloaded

  • output_dir (str) – the directory in which the files will be saved


the metadata of the model

Return type:


SAClient.run_prediction(project, images_list, model)#
This function runs smart prediction on given list of images from a given project

using the neural network of your choice

  • project (str or dict) – the project in which the target images are uploaded.

  • images_list (list of str) – the list of image names on which smart prediction has to be run

  • model (str or dict) – the name of the model that should be used for running smart prediction


tuple of two lists, list of images on which the prediction has succeeded and failed respectively

Return type:


SAClient.search_models(name=None, type_=None, project_id=None, task=None, include_global=True)#

Search for ML models.

  • name (str) – search string

  • type_ (str) – ml model type string

  • project_id (int) – project id

  • task (str) – training task

  • include_global (bool) – include global ml models


ml model metadata

Return type:

list of dicts