Vision - Classification 2D

Category: task.vision.classification.2d
Version: 1.0.2
UUID: 455c0b5bb7e05df06050f9d158c2d0080dcf10fd5307abc8adb1633d98411f41
Execution context: leip_af
Choice Priority: 999.9

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 grayscale	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. Data output width has to match model input width (model.input.width == target.width)
2. Data output height has to match model input height (model.input.height == target.height)
3. Model has to be a 2D classification model: model \(\in\) model.classification.2d
4. Data generator has to be a 2D classification data generator: data_generator \(\in\) data_generator.vision.classification.2d
5. Evaluation has use a 2D detection metric: metric \(\in\) metric.classification.2d
6. Export Data has to use a 2D classification format: export_data \(\in\) export_data.classification.2d
7. Data report has to be a 2D classification report: data_report \(\in\) data_report.classification.2d

This component fits into

Name	UUID	Synonyms
Full Recipe	3d945b...	task