Tuning a Semantic Segmentation 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.

Metrics Computed by the Semantic Segmentation Algorithm

The semantic segmentation algorithm reports two validation metrics. When tuning hyperparameter values, choose one of these metrics as the objective.

Metric Name	Description	Optimization Direction
validation:mIOU	The area of the intersection of the predicted segmentation and the ground truth divided by the area of union between them for images in the validation set. Also known as the Jaccard Index.	Maximize
validation:pixel_accuracy	The percentage of pixels that are correctly classified in images from the validation set.	Maximize

Tunable Semantic Segmentation Hyperparameters

You can tune the following hyperparameters for the semantic segmentation algorithm.

Parameter Name	Parameter Type	Recommended Ranges
learning_rate	ContinuousParameterRange	MinValue: 1e-4, MaxValue: 1e-1
mini_batch_size	IntegerParameterRanges	MinValue: 1, MaxValue: 128
momentum	ContinuousParameterRange	MinValue: 0.9, MaxValue: 0.999
optimzer	CategoricalParameterRanges	['sgd', 'adam', 'adadelta']
weight_decay	ContinuousParameterRange	MinValue: 1e-5, MaxValue: 1e-3