Machine Learning Models Product¶
Metadata¶
Data product name |
DpdMerMachineLearningModels |
Data product custodian |
MER |
Name of the Schema file |
|
Last Edited for DPDD Version |
2.0 |
Processing Element(s) creating/using the data product |
Creators:
scripts. Consumers:
|
Processing function using the data product |
MER |
Proposed for inclusion in EAS/SAS |
This product is proposed for inclusion in the SAS: maybe. This product contains trained machine learning models that are used in different steps of the MER pipeline. It could be useful to have them in the SAS in case scientists want to replicate some of the MER classifications. |
Data Product Elements¶
Header |
object of type sys:genericHeader |
Data |
object of type mer:merMachineLearningModels |
QualityFlags |
object of type dqc:sqfPlaceHolder |
Parameters |
object of type ppr:genericKeyValueParameters |
Detailed Description of the Data Product¶
This product is an input to the MER Pipeline. It contains a list of trained machine learning (ML) models used in one or several of the MER processing steps.
The product currently contains a ML model trained to detect spurious sources, and the Zoobot model used to classify sources according to their morphology (see the MER morphology cookbook for more details).
The main elements inside this product are:
TileIndex (optional): The MER tile index to which the machine learning models should be associated.
PatchId (optional): The sky patch id to which the machine learning models should be associated.
CalblockId (optional): The calibration block id to which the machine learning models should be associated.
CalblockVariant (optional): The calibration block variant to which the machine learning models should be associated.
ProcessingMode (optional): The MER pipeline processing mode (e.g. WIDE, DEEP) to which the machine learning models should be associated.
Model (one to infinity): The model name and the file containing the trained model.