# High Detail Mode Training Parameters

The training tool parameters control the training of Green Classify Tool in High Detail mode

Parameter | Description
--- | ---
Epoch Count | The number of times to train using all training images. Higher the value more the repetition. The value is from 1 through 1000000000. Tip: High Detail mode uses different concept of Epoch Count from Focused mode. You’d better use higher epoch count in High Detail mode than in Focused mode with same database.
Training Set | The dataset that is used to create a deep learning model. This means that during the deep learning, only the features of the images included in the train set are extracted to create a deep learning model. You can select training set in Select Training Set dialog.
Validation Set Ratio | The ratio of number of views which will be used as a validation set among training set. You can enter validation set ratio value from 1% to 50%. If you increase the validation set ratio while keeping the number of training set, the amount of data used for training will decrease. So, setting a high validation set ratio could affect the performance negatively when you are using small training set. On the other hands, too low validation set ratio will not be helpful for selecting good model for unseen data set.
Minimum Epochs | Due to the validation set, High Detail mode can select the model created from low epoch as a final model to prevent overfitting. If you set a minimum epoch, High Detail mode selects a model created after this epoch as a final model. If you get a good processing result to the train set, but not a good one to the test set, you may think overfitting. However, if the processing result is not good to the train and test set, it would be underfitting. In this case, it is recommended to use higher minimum epochs. The available value is 0 through current epoch.
Patience | Each time a fixed number of iterations had executed, High Detail mode measures loss of the model. If no drop of loss is observed for N(=value of patience) times of 128 iterations. High Detail mode stops training and select the model with lowest loss until the epoch. You can utilize patience for early stopping in case of no need to proceed a training process unless loss drops over a certain period. The available value is 0 through 1000000000. Tip: Iteration means neural network is trained with one batch of images. This is different with Epoch and set based on following equation: Epoch = (Batch size) x (total iteration) For example, if you have 100 images and having batch size of 10, then you need 10 iteration for 1 Epoch. In here, ‘Epoch’ is set by user and ‘Batch size’ is automatically set by considering the GPU capacity. So, it means iteration is also set automatically depending on Batch size. If it is complicated to set a specific epoch count, you can increase the epoch count and create a model based on patience. You can tract the loss for every epoch in Log Messages. The Loss is calculated with the validation set but not training set.
Class Weights | The weights are used to apply different batch sampling for each class. When there are imbalances in the number of views per class (tag), you may use class weight to put different weights on each class for training. For example, the ratio of Class 1 to Class 2 as 2:1 means that the number of training images for Class 1 is as twice as Class 2. To set class weights, click the hyperlink text of class weight in tool parameter panel and open the class weights dialog. If you double-click the weight cell of the list, you can edit the class weight. You cannot put decimal number in here (only natural numbers).