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Implement valor_core
to compute metrics locally via numpy
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Improvements
valor_core
, which can evaluate classification and object detection tasks locally in numpy without using postgres. It defines two functions,evaluate_classification
andevaluate_detection
, which takeGroundTruth
andPrediction
objects and produce anEvaluation
object that's equivalent to what's created from the API + client today.ValorDetectionManager
Testing
evaluate_detection
andevaluate_classification
pass all integration and functional tests from Valor; these tests are implemented incore/test/
. Current test coverage is 91%.core/benchmarks/
contains equivalent benchmark scripts to what's used forvalor
Next Steps in Future PRs
ValorClassificationManager
evaluate_segmentation
andValorSegmentationManager
Usage Examples from
core/README.md
Passing Lists of GroundTruth and Prediction Objects
The first way to use
valor_core
is to pass a list of groundtruth and prediction objects to anevaluate_...
function, like so:Passing DataFrames
The second way to use
valor_core
is to pass in a dataframe of groundtruths and predictions:Using a Data Manager
Finally, you can use a manager class (i.e.,
ValorDetectionManager
) to run your evaluation. The advantage to using a manager class is a) you won't have to keep all annotation types in memory in a large list and b) we can pre-compute certain columns (i.e.,iou
) in advance of the.evaluate()
call.