public sealed class AutoMlImageSegmentationInputs : IMessage<AutoMlImageSegmentationInputs>, IEquatable<AutoMlImageSegmentationInputs>, IDeepCloneable<AutoMlImageSegmentationInputs>, IBufferMessage, IMessage
The ID of the base model. If it is specified, the new model will be
trained based on the base model. Otherwise, the new model will be
trained from scratch. The base model must be in the same
Project and Location as the new Model to train, and have the same
modelType.
The training budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour. The actual
metadata.costMilliNodeHours will be equal or less than this value.
If further model training ceases to provide any improvements, it will
stop without using the full budget and the metadata.successfulStopReason
will be model-converged.
Note, node_hour = actual_hour * number_of_nodes_involved. Or
actaul_wall_clock_hours = train_budget_milli_node_hours /
(number_of_nodes_involved * 1000)
For modelType cloud-high-accuracy-1(default), the budget must be between
20,000 and 2,000,000 milli node hours, inclusive. The default value is
192,000 which represents one day in wall time
(1000 milli * 24 hours * 8 nodes).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-20 UTC."],[[["\u003cp\u003eThis webpage provides documentation for the \u003ccode\u003eAutoMlImageSegmentationInputs\u003c/code\u003e class within the Google Cloud AI Platform V1, specifically detailing its usage for training image segmentation models.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eAutoMlImageSegmentationInputs\u003c/code\u003e class, part of the \u003ccode\u003eGoogle.Cloud.AIPlatform.V1.Schema.TrainingJob.Definition\u003c/code\u003e namespace, is used to define inputs for automated machine learning (AutoML) image segmentation training jobs.\u003c/p\u003e\n"],["\u003cp\u003eThe page lists the history of the \u003ccode\u003eAutoMlImageSegmentationInputs\u003c/code\u003e class, from version 1.0.0 all the way to 3.22.0, with 3.22.0 being the latest, and links to each of the version's respective documentation.\u003c/p\u003e\n"],["\u003cp\u003eKey properties of the \u003ccode\u003eAutoMlImageSegmentationInputs\u003c/code\u003e class include \u003ccode\u003eBaseModelId\u003c/code\u003e, which allows training a new model based on an existing one, \u003ccode\u003eBudgetMilliNodeHours\u003c/code\u003e, defining the training budget in milli node hours, and \u003ccode\u003eModelType\u003c/code\u003e which determines the model's accuracy.\u003c/p\u003e\n"],["\u003cp\u003eThis \u003ccode\u003eAutoMlImageSegmentationInputs\u003c/code\u003e class implements several interfaces, such as \u003ccode\u003eIMessage\u003c/code\u003e, \u003ccode\u003eIEquatable\u003c/code\u003e, \u003ccode\u003eIDeepCloneable\u003c/code\u003e, and \u003ccode\u003eIBufferMessage\u003c/code\u003e, and has two constructors: a default constructor, and one taking an \u003ccode\u003eAutoMlImageSegmentationInputs\u003c/code\u003e object as a parameter.\u003c/p\u003e\n"]]],[],null,[]]
