Tune an Object Detection - TensorFlow model

Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. You choose the tunable hyperparameters, a range of values for each, and an objective metric. You choose the objective metric from the metrics that the algorithm computes. Automatic model tuning searches the hyperparameters chosen to find the combination of values that result in the model that optimizes the objective metric.

For more information about model tuning, see Automatic model tuning with SageMaker AI.

Metrics computed by the Object Detection - TensorFlow algorithm

Refer to the following chart to find which metrics are computed by the Object Detection - TensorFlow algorithm.

Metric Name	Description	Optimization Direction	Regex Pattern
validation:localization_loss	The localization loss for box prediction.	Minimize	Val_localization=([0-9\\.]+)

Tunable Object Detection - TensorFlow hyperparameters

Tune an object detection model with the following hyperparameters. The hyperparameters that have the greatest impact on object detection objective metrics are: batch_size, learning_rate, and optimizer. Tune the optimizer-related hyperparameters, such as momentum, regularizers_l2, beta_1, beta_2, and eps based on the selected optimizer. For example, use beta_1 and beta_2 only when adam is the optimizer.

For more information about which hyperparameters are used for each optimizer, see Object Detection - TensorFlow Hyperparameters.

Parameter Name	Parameter Type	Recommended Ranges
batch_size	IntegerParameterRanges	MinValue: 8, MaxValue: 512
beta_1	ContinuousParameterRanges	MinValue: 1e-6, MaxValue: 0.999
beta_2	ContinuousParameterRanges	MinValue: 1e-6, MaxValue: 0.999
eps	ContinuousParameterRanges	MinValue: 1e-8, MaxValue: 1.0
learning_rate	ContinuousParameterRanges	MinValue: 1e-6, MaxValue: 0.5
momentum	ContinuousParameterRanges	MinValue: 0.0, MaxValue: 0.999
optimizer	CategoricalParameterRanges	['sgd', ‘adam’, ‘rmsprop’, 'nesterov', 'adagrad', 'adadelta']
regularizers_l2	ContinuousParameterRanges	MinValue: 0.0, MaxValue: 0.999
train_only_on_top_layer	CategoricalParameterRanges	['True', 'False']
initial_accumulator_value	CategoricalParameterRanges	MinValue: 0.0, MaxValue: 0.999