Evaluate semantic segmentation data set against ground truth

computes various metrics to evaluate the quality of the semantic segmentation
results from confusion matrices, `ssm`

= evaluateSemanticSegmentation(`imageSetConfusion`

,`classNames`

)`imageSetConfusion`

, with
segmentation classes `classNames`

.

`[`

computes various metrics to evaluate the quality of the block-based semantic
segmentation results from confusion matrices, `ssm`

,`blockMetrics`

] = evaluateSemanticSegmentation(`blockSetConfusion`

,`classNames`

)`blockSetConfusion`

with classes `classNames`

.

`[___] = evaluateSemanticSegmentation(___,`

computes semantic segmentation metrics using one or more
`Name,Value`

)`Name,Value`

pair arguments to control the evaluation.

[1] Csurka, G., D. Larlus, and F. Perronnin. "What is a good evaluation measure for
semantic segmentation?" *Proceedings of the British Machine Vision
Conference*, 2013, pp. 32.1–32.11.

`bfscore`

|`jaccard`

|`segmentationConfusionMatrix`

|`semanticseg`

|`plotconfusion`

(Deep Learning Toolbox)

- Calculate Segmentation Metrics in Block-Based Workflow
- Getting Started with Semantic Segmentation Using Deep Learning
- Deep Learning in MATLAB (Deep Learning Toolbox)
- Datastores for Deep Learning (Deep Learning Toolbox)