- 1.73.0 (latest)
- 1.72.0
- 1.71.1
- 1.70.0
- 1.69.0
- 1.68.0
- 1.67.1
- 1.66.0
- 1.65.0
- 1.63.0
- 1.62.0
- 1.60.0
- 1.59.0
- 1.58.0
- 1.57.0
- 1.56.0
- 1.55.0
- 1.54.1
- 1.53.0
- 1.52.0
- 1.51.0
- 1.50.0
- 1.49.0
- 1.48.0
- 1.47.0
- 1.46.0
- 1.45.0
- 1.44.0
- 1.43.0
- 1.39.0
- 1.38.1
- 1.37.0
- 1.36.4
- 1.35.0
- 1.34.0
- 1.33.1
- 1.32.0
- 1.31.1
- 1.30.1
- 1.29.0
- 1.28.1
- 1.27.1
- 1.26.1
- 1.25.0
- 1.24.1
- 1.23.0
- 1.22.1
- 1.21.0
- 1.20.0
- 1.19.1
- 1.18.3
- 1.17.1
- 1.16.1
- 1.15.1
- 1.14.0
- 1.13.1
- 1.12.1
- 1.11.0
- 1.10.0
- 1.9.0
- 1.8.1
- 1.7.1
- 1.6.2
- 1.5.0
- 1.4.3
- 1.3.0
- 1.2.0
- 1.1.1
- 1.0.1
- 0.9.0
- 0.8.0
- 0.7.1
- 0.6.0
- 0.5.1
- 0.4.0
- 0.3.1
AutoMlImageObjectDetectionInputs
(
mapping
=
None
,
*
,
ignore_unknown_fields
=
False
,
**
kwargs
)
Attributes
budget_milli_node_hours
int
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. For modelType cloud
\ (default), the budget must be between 20,000 and
900,000 milli node hours, inclusive. The default value is
216,000 which represents one day in wall time, considering 9
nodes are used. For model types mobile-tf-low-latency-1
, mobile-tf-versatile-1
, mobile-tf-high-accuracy-1
the
training 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 on a single node that is
used.disable_early_stopping
bool
Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Object Detection might stop training before the entire training budget has been used.
Classes
ModelType
ModelType
(
value
)
Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD_HIGH_ACCURACY_1 (1): A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should also have a higher prediction quality than other cloud models. CLOUD_LOW_LATENCY_1 (2): A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a low latency, but may have lower prediction quality than other cloud models. MOBILE_TF_LOW_LATENCY_1 (3): A model that, in addition to being available within Google Cloud can also be exported (see ModelService.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 mobile models. MOBILE_TF_VERSATILE_1 (4): A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. MOBILE_TF_HIGH_ACCURACY_1 (5): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.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 mobile models.
Methods
AutoMlImageObjectDetectionInputs
AutoMlImageObjectDetectionInputs
(
mapping
=
None
,
*
,
ignore_unknown_fields
=
False
,
**
kwargs
)
