EClassificationDataset::SplitDatasetForLocator
Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.
Namespace: Euresys::Open_eVision::EasyDeepLearning
[C++]
void SplitDatasetForLocator(
EClassificationDataset& d1,
EClassificationDataset& d2,
float proportion,
bool random
)
Parameters
d1
First part of the dataset
d2
Second part of the dataset
proportion
Proportion of image of each class to put into the first part. The remaining images are put in d2
random
Randomly sample the images.
Remarks
The method ensures that all the object labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.
EClassificationDataset.SplitDatasetForLocator
Splits the dataset in two parts for training and validation of a ELocator tool. Images without object labeling are excluded from the split.
Namespace: Euresys.Open_eVision.EasyDeepLearning
[C#]
void SplitDatasetForLocator(
Euresys.Open_eVision.EasyDeepLearning.EClassificationDataset d1,
Euresys.Open_eVision.EasyDeepLearning.EClassificationDataset d2,
float proportion,
bool random
)
Parameters
d1
First part of the dataset
d2
Second part of the dataset
proportion
Proportion of image of each class to put into the first part. The remaining images are put in d2
random
Randomly sample the images.
Remarks
The method ensures that all the object labels are represented in d1. Thus, even when the parameter random is set to false, the images in d1 and d2 can be ordered differently than they were in the original dataset.