model_evaluation#
Module containing the model evaluation checks in the vision package.
Classes
Calculate prediction drift between train dataset and test dataset, using statistical measures. |
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Summarize given metrics on a dataset and model. |
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Calculate the confusion matrix of the model on the given dataset. |
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Segment the data by various properties of the image, and compare the performance of the segments. |
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Summarize mean average precision metrics on a dataset and model per IoU and bounding box area. |
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Summarize mean average recall metrics on a dataset and model per detections and area range. |
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Find the properties that best split the data into segments of high and low model error. |
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Compare performance of model on original dataset and augmented dataset. |
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Compare given model score to simple model score (according to given model type). |
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Calculate performance metrics of a given model on a given dataset. |