Vision - Segmentation 2D

Category: task.vision.segmentation.2d
Version: 1.0.1
UUID: 4a3beab73753a29588ea2ddfe2193c8d5ea75dc16f133c6d5a18bd8a7b1e1c38
Execution context: leip_af

Value Parameters

Name	Synonyms	Type	Values	Help
Training Batch Size	train.batch_size_train	scalar	int, min: 1
Validation Batch Size	train.batch_size_val	scalar	int, min: 1
Random Number Generator Seed	global_seed, task.global_seed, experiment.seed	scalar	int
Target Image Height	target.height	scalar	int, min: 1
Experiment Name	experiment.name	scalar	string
Normalization Scheme	data.normalization	choice	imagenet inception yolo	The normalization is applied at augmentation time. These names are proxies to the mean and standard deviation to be applied to the images in the dataset. The yolo option applies no normalization at all.
Number of parallel workers	train.num_workers	scalar	int, min: 0
Target Image Width	target.width	scalar	int

Constraints

1. Model has to be a 2D segmentation model: model \(\in\) model.segmentation.2d
2. Data generator has to be a 2D segmentation data generator: data_generator \(\in\) data_generator.vision.segmentation.2d
3. Evaluation has use a 2D segmentation metric: metric \(\in\) metric.segmentation.2d
4. Data report has to use a 2D segmentation format: data_report \(\in\) data_report.segmentation.2d

This component fits into

Name	UUID	Synonyms
Full Recipe	062a2f...	task