public sealed class ImageClassificationModelMetadata : IMessage<ImageClassificationModelMetadata>, IEquatable<ImageClassificationModelMetadata>, IDeepCloneable<ImageClassificationModelMetadata>, IBufferMessage, IMessage
Reference documentation and code samples for the Google AutoML v1 API class ImageClassificationModelMetadata.
Optional. The ID of the base model. If it is specified, the new model
will be created based on the base model. Otherwise, the new model will be
created from scratch. The base model must be in the same
project and location as the new model to create, and have the same
model_type.
Optional. Type of the model. The available values are:
cloud - Model to be used via prediction calls to AutoML API.
This is the default value.
mobile-low-latency-1 - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards. Expected to have low latency, but
may have lower prediction quality than other models.
mobile-versatile-1 - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards.
mobile-high-accuracy-1 - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
with TensorFlow afterwards. Expected to have a higher
latency, but should also have a higher prediction quality
than other models.
mobile-core-ml-low-latency-1 - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
ML afterwards. Expected to have low latency, but may have
lower prediction quality than other models.
mobile-core-ml-versatile-1 - A model that, in addition to providing
prediction via AutoML API, can also be exported (see
[AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
ML afterwards.
mobile-core-ml-high-accuracy-1 - A model that, in addition to
providing prediction via AutoML API, can also be exported
(see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with
Core ML afterwards. Expected to have a higher latency, but
should also have a higher prediction quality than other
models.
Output only. The number of nodes this model is deployed on. A node is an
abstraction of a machine resource, which can handle online prediction QPS
as given in the node_qps field.
public long TrainBudgetMilliNodeHours { get; set; }
Optional. The train budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour. The actual
train_cost will be equal or less than this value. If further model
training ceases to provide any improvements, it will stop without using
full budget and the stop_reason will be MODEL_CONVERGED.
Note, node_hour = actual_hour * number_of_nodes_invovled.
For model type cloud(default), the train budget must be between 8,000
and 800,000 milli node hours, inclusive. The default value is 192, 000
which represents one day in wall time. For model type
mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1,
mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1,
mobile-core-ml-high-accuracy-1, the train budget must be between 1,000
and 100,000 milli node hours, inclusive. The default value is 24, 000 which
represents one day in wall time.
Output only. The actual train cost of creating this model, expressed in
milli node hours, i.e. 1,000 value in this field means 1 node hour.
Guaranteed to not exceed the train budget.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[[["\u003cp\u003eThe latest version of the \u003ccode\u003eImageClassificationModelMetadata\u003c/code\u003e class is 3.4.0, part of the Google AutoML v1 API.\u003c/p\u003e\n"],["\u003cp\u003eThis class, \u003ccode\u003eImageClassificationModelMetadata\u003c/code\u003e, is used to manage metadata for image classification models within the Google AutoML service.\u003c/p\u003e\n"],["\u003cp\u003eThis class allows for the specification of various parameters such as \u003ccode\u003eBaseModelId\u003c/code\u003e, \u003ccode\u003eModelType\u003c/code\u003e, and training budget expressed in milli node hours and also includes properties about the node it's deployed on.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eModelType\u003c/code\u003e property supports various model types, including those optimized for mobile and edge devices, like \u003ccode\u003emobile-low-latency-1\u003c/code\u003e and \u003ccode\u003emobile-core-ml-high-accuracy-1\u003c/code\u003e, as well as cloud deployment.\u003c/p\u003e\n"],["\u003cp\u003eIt implements multiple interfaces like \u003ccode\u003eIMessage\u003c/code\u003e, \u003ccode\u003eIEquatable\u003c/code\u003e, \u003ccode\u003eIDeepCloneable\u003c/code\u003e, and \u003ccode\u003eIBufferMessage\u003c/code\u003e to give the class a wide range of functionality.\u003c/p\u003e\n"]]],[],null,["# Google AutoML v1 API - Class ImageClassificationModelMetadata (3.4.0)\n\nVersion latestkeyboard_arrow_down\n\n- [3.4.0 (latest)](/dotnet/docs/reference/Google.Cloud.AutoML.V1/latest/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [3.3.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/3.3.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [3.2.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/3.2.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [3.1.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/3.1.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [3.0.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/3.0.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [2.6.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/2.6.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [2.5.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/2.5.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [2.4.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/2.4.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [2.3.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/2.3.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata)\n- [2.2.0](/dotnet/docs/reference/Google.Cloud.AutoML.V1/2.2.0/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata) \n\n public sealed class ImageClassificationModelMetadata : IMessage\u003cImageClassificationModelMetadata\u003e, IEquatable\u003cImageClassificationModelMetadata\u003e, IDeepCloneable\u003cImageClassificationModelMetadata\u003e, IBufferMessage, IMessage\n\nReference documentation and code samples for the Google AutoML v1 API class ImageClassificationModelMetadata.\n\nModel metadata for image classification. \n\nInheritance\n-----------\n\n[object](https://learn.microsoft.com/dotnet/api/system.object) \\\u003e ImageClassificationModelMetadata \n\nImplements\n----------\n\n[IMessage](https://cloud.google.com/dotnet/docs/reference/Google.Protobuf/latest/Google.Protobuf.IMessage-1.html)[ImageClassificationModelMetadata](/dotnet/docs/reference/Google.Cloud.AutoML.V1/latest/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata), [IEquatable](https://learn.microsoft.com/dotnet/api/system.iequatable-1)[ImageClassificationModelMetadata](/dotnet/docs/reference/Google.Cloud.AutoML.V1/latest/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata), [IDeepCloneable](https://cloud.google.com/dotnet/docs/reference/Google.Protobuf/latest/Google.Protobuf.IDeepCloneable-1.html)[ImageClassificationModelMetadata](/dotnet/docs/reference/Google.Cloud.AutoML.V1/latest/Google.Cloud.AutoML.V1.ImageClassificationModelMetadata), [IBufferMessage](https://cloud.google.com/dotnet/docs/reference/Google.Protobuf/latest/Google.Protobuf.IBufferMessage.html), [IMessage](https://cloud.google.com/dotnet/docs/reference/Google.Protobuf/latest/Google.Protobuf.IMessage.html) \n\nInherited Members\n-----------------\n\n[object.GetHashCode()](https://learn.microsoft.com/dotnet/api/system.object.gethashcode) \n[object.GetType()](https://learn.microsoft.com/dotnet/api/system.object.gettype) \n[object.ToString()](https://learn.microsoft.com/dotnet/api/system.object.tostring)\n\nNamespace\n---------\n\n[Google.Cloud.AutoML.V1](/dotnet/docs/reference/Google.Cloud.AutoML.V1/latest/Google.Cloud.AutoML.V1)\n\nAssembly\n--------\n\nGoogle.Cloud.AutoML.V1.dll\n\nConstructors\n------------\n\n### ImageClassificationModelMetadata()\n\n public ImageClassificationModelMetadata()\n\n### ImageClassificationModelMetadata(ImageClassificationModelMetadata)\n\n public ImageClassificationModelMetadata(ImageClassificationModelMetadata other)\n\nProperties\n----------\n\n### BaseModelId\n\n public string BaseModelId { get; set; }\n\nOptional. The ID of the `base` model. If it is specified, the new model\nwill be created based on the `base` model. Otherwise, the new model will be\ncreated from scratch. The `base` model must be in the same\n`project` and `location` as the new model to create, and have the same\n`model_type`.\n\n### ModelType\n\n public string ModelType { get; set; }\n\nOptional. Type of the model. The available values are:\n\n- `cloud` - Model to be used via prediction calls to AutoML API. This is the default value.\n- `mobile-low-latency-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see \\[AutoMl.ExportModel\\]\\[google.cloud.automl.v1.AutoMl.ExportModel\\]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.\n- `mobile-versatile-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see \\[AutoMl.ExportModel\\]\\[google.cloud.automl.v1.AutoMl.ExportModel\\]) and used on a mobile or edge device with TensorFlow afterwards.\n- `mobile-high-accuracy-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see \\[AutoMl.ExportModel\\]\\[google.cloud.automl.v1.AutoMl.ExportModel\\]) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.\n- `mobile-core-ml-low-latency-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see \\[AutoMl.ExportModel\\]\\[google.cloud.automl.v1.AutoMl.ExportModel\\]) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models.\n- `mobile-core-ml-versatile-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see \\[AutoMl.ExportModel\\]\\[google.cloud.automl.v1.AutoMl.ExportModel\\]) and used on a mobile device with Core ML afterwards.\n- `mobile-core-ml-high-accuracy-1` - A model that, in addition to providing prediction via AutoML API, can also be exported (see \\[AutoMl.ExportModel\\]\\[google.cloud.automl.v1.AutoMl.ExportModel\\]) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.\n\n### NodeCount\n\n public long NodeCount { get; set; }\n\nOutput only. The number of nodes this model is deployed on. A node is an\nabstraction of a machine resource, which can handle online prediction QPS\nas given in the node_qps field.\n\n### NodeQps\n\n public double NodeQps { get; set; }\n\nOutput only. An approximate number of online prediction QPS that can\nbe supported by this model per each node on which it is deployed.\n\n### StopReason\n\n public string StopReason { get; set; }\n\nOutput only. The reason that this create model operation stopped,\ne.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.\n\n### TrainBudgetMilliNodeHours\n\n public long TrainBudgetMilliNodeHours { get; set; }\n\nOptional. The train budget of creating this model, expressed in milli node\nhours i.e. 1,000 value in this field means 1 node hour. The actual\n`train_cost` will be equal or less than this value. If further model\ntraining ceases to provide any improvements, it will stop without using\nfull budget and the stop_reason will be `MODEL_CONVERGED`.\nNote, node_hour = actual_hour \\* number_of_nodes_invovled.\nFor model type `cloud`(default), the train budget must be between 8,000\nand 800,000 milli node hours, inclusive. The default value is 192, 000\nwhich represents one day in wall time. For model type\n`mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`,\n`mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`,\n`mobile-core-ml-high-accuracy-1`, the train budget must be between 1,000\nand 100,000 milli node hours, inclusive. The default value is 24, 000 which\nrepresents one day in wall time.\n\n### TrainCostMilliNodeHours\n\n public long TrainCostMilliNodeHours { get; set; }\n\nOutput only. The actual train cost of creating this model, expressed in\nmilli node hours, i.e. 1,000 value in this field means 1 node hour.\nGuaranteed to not exceed the train budget."]]
