ImageLoadingEstimator Class
Definition
Important
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public sealed class ImageLoadingEstimator : Microsoft.ML.Data.TrivialEstimator<Microsoft.ML.Data.ImageLoadingTransformer>type ImageLoadingEstimator = class
inherit TrivialEstimator<ImageLoadingTransformer>Public NotInheritable Class ImageLoadingEstimator
Inherits TrivialEstimator(Of ImageLoadingTransformer)- Inheritance
Remarks
Estimator Characteristics
| Does this estimator need to look at the data to train its parameters? | No |
| Input column data type | Text |
| Output column data type | MLImage |
| Required NuGet in addition to Microsoft.ML | Microsoft.ML.ImageAnalytics |
| Exportable to ONNX | No |
The resulting ImageLoadingTransformer creates a new column, named as specified in the output column name parameters, and loads in it images specified in the input column. Loading is the first step of almost every pipeline that does image processing, and further analysis on images. The images to load need to be in the formats supported by MLImage implementation. For end-to-end image processing pipelines, and scenarios in your applications, see the examples in the machinelearning-samples github repository.
Check the See Also section for links to usage examples.
Methods
| Fit(IDataView) | (Inherited from TrivialEstimator<TTransformer>) |
| GetOutputSchema(SchemaShape) |
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline. |
Extension Methods
| AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment) |
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes. |
| WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>) |
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called. |