Get Detection Model Learning Curve

Response Body

Name	Type	Description	Available Version
`data`	array	Array of `learningcurve` objects.	2.0
`object`	string	Object returned; in this case, `list`.	2.0

learningCurve Response Body

Name	Type	Description	Available Version
`epoch`	int	Epoch to which the metrics correspond.	2.0
`epochResults`	object	N/A	2.0
`metricsData`	object	Model metrics values.	2.0
`object`	string	Object returned; in this case, `learningcurve`.	2.0

metricsData Response Body

Name	Type	Description	Available Version
`labelMetrics`	array	Array that contains the metrics for each label in the model.	2.0

classMetrics Response Body

Name	Type	Description	Available Version
`averagePrecision`	float	Average precision for the specified label. This value is computed from the validation data. The PASCAL VOC 2012 method is used to compute the average precision, also known as [email protected] When no examples for a label appear in the validation data, this value returns null.	2.0
`label`	string	Label for which the metrics are returned.	2.0
`f1`	array	The weighted average of precision and recall for the specified label. This value is computed from the validation data.	2.0
`precision`	array	Array of precision values used to compute the average precision. The precision array hasn't been preprocessed to be monotonically non-increasing. The precision and recall arrays can be used to draw the precision-recall curve.	2.0
`recall`	array	Array of recall values used to compute the average precision.	2.0

You can use the learning curve data to see how a model performed in each training iteration. This information helps you tune your model and identify the optimal number of epochs to specify when you train a dataset.

When you train a dataset, you can specify the number of epochs used to create the model, or if you don't specify any epochs, the training call uses a default number of epochs. In each epoch, machine learning happens behind the scenes to create the model. Information from an epoch is passed into the next epoch, from that epoch to the next, and so on.

When it comes to epochs, more isn't always better. For example, you could train a dataset and specify 30 epochs. Using the learning curve data for the resulting model, you might see that the most accurate results for that dataset are in the 23rd epoch. You can use the learning curve data to see if your model is overfit (model predicts accurately with training data but not unseen data) or underfit (model doesn't predict accurately with training data or unseen data).

Query Parameters

By default, this call returns 25 epochs. To page through the epochs for a model, use the offset and count query parameters.

Name	Description	Available Version
`count`	Number of epochs to return. Maximum valid value is 25. If you specify a number greater than 25, the call returns 25 epochs. Optional.	1.0
`offset`	Index of the epoch from which you want to start paging. Optional.	1.0

Here's an example of these query parameters. If you omit the count parameter, the API returns 25 epochs. If you omit the offset parameter, paging starts at 0.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache"  "https://api.einstein.ai/v2/vision/models/7JPFL6UKMHUEJLHJ6FEX4YBV4U/lc?offset=30&count=10"

For example, let's say you want to page through all of the model epochs and show 10 at a time. The first call would have offset=0 and count=10, the second call would have offset=10 and count=10, and so on.