Training a Deep Learning Tool

In the API, to train a deep learning tool, call the EDeepLearningTool::Train(trainingDataset, validationDataset, numberOfIterations) method.

● An Iteration corresponds to going through all the images in the training dataset once.
□ The training process requires a large number of iterations to obtain good results.
□ The training process requires a large number of iterations to obtain good results.
□ The default number of iterations is 50.
□ The larger the number of iterations, the longer the training is and the better the results you obtain.
● Multiple iterations:
□ Calling the EDeepLearningTool::Train method several times with the same training and validation dataset is equivalent to calling it once but with a larger number of iterations.
□ Call EDeepLearningTool::GetNumTrainedIterations to get the total number of iterations used to train the classifier.
□ In successive calls to EDeepLearningTool::Train:

- You can add images to the training and validation dataset to train the tool to recognize new instances of your problem.

- We do not recommend that you remove images from the dataset as the tool might forget about these images during the new training phase.

● The training process is asynchronous:
□ EDeepLearningTool::Train launches a new thread that does the training in background.
□ EDeepLearningTool::WaitForTrainingCompletion suspends the program until the whole training is completed.
□ EDeepLearningTool::WaitForIterationCompletion suspends the program until the current iteration is completed.
□ During the training, EDeepLearningTool::GetCurrentTrainingProgression shows the progression of the training.
● The batch size corresponds to the number of image patches that are processed together.
□ The training is influenced by the batch size.
□ A large batch size increases the processing speed of a single iteration on a GPU but requires more memory.
□ The training process is not able to learn a good model with too small batch sizes.
□ By default, the batch size is determined automatically during the training to optimize the training speed with respect to the available memory.

- Use EDeepLearningTool::SetOptimizeBatchSize(false) to disable this behavior.

- Use EDeepLearningTool::SetBatchSize to change the size of your batch.

□ EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed gets the batch size that maximizes the batch classification speed on a GPU according to the available memory.
□ It is common to choose powers of 2 as the batch size for performance reasons.