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
Algorithm
The object detection algorithm reports on a single metric during training:
validation:mAP. When tuning a model, choose this metric as the
objective metric.
| Metric Name | Description | Optimization Direction |
|---|---|---|
validation:mAP |
Mean Average Precision (mAP) computed on the validation set. |
Maximize |
Tunable Object Detection
Hyperparameters
Tune the Amazon SageMaker AI object detection model with the following hyperparameters. The
hyperparameters that have the greatest impact on the object detection objective
metric are: mini_batch_size, learning_rate, and
optimizer.
| Parameter Name | Parameter Type | Recommended Ranges |
|---|---|---|
learning_rate |
ContinuousParameterRange |
MinValue: 1e-6, MaxValue: 0.5 |
mini_batch_size |
IntegerParameterRanges |
MinValue: 8, MaxValue: 64 |
momentum |
ContinuousParameterRange |
MinValue: 0.0, MaxValue: 0.999 |
optimizer |
CategoricalParameterRanges |
['sgd', 'adam', 'rmsprop', 'adadelta'] |
weight_decay |
ContinuousParameterRange |
MinValue: 0.0, MaxValue: 0.999 |
