# Red Analyze Tool High Detail Mode Training Parameters

The training tool parameters control the training of Red Analyze Tool in High Detail mode.

Note:

- The Stride and Patch Size parameters are only available when Expert Mode is enabled. This is enabled via the Help menu.

Parameter | Description
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Epoch Count | The number of times to train using all training images. Higher the value more the repetition. The value is from 1 through 100000. Tip: High Detail mode uses a different concept of Epoch Count from Focused mode. You’d better use a higher epoch count in High Detail mode than in Focused mode with the same database.
Training Set | The dataset that is used to create a deep learning model. This means that during 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 the number of views which will be used as a validation set among training set. You can enter the validation set ratio value from 1% to 50%. If you increase the validation set ratio while keeping the number of the 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 a small training set. On the other hand, a too low validation set ratio will not be helpful for selecting a good model for an unseen data set.
Minimum Epochs | Due to the validation set, High Detail mode can select the model created from the 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 the current epoch.
Patience Epochs | Each time a fixed number of iterations is executed, High Detail mode measures the loss of the model. If no drop of loss is observed for N (the value of Patience Epochs) epochs, High Detail mode stops training and selects the model with the lowest loss until the epoch. You can utilize patience epoch for early stopping in case that there is no need to proceed a training process unless loss drops over a certain period. The available value is 0 through 100000. Tip: 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 with Tool - Inspect Loss. The Loss is calculated with the validation set but not training set.
Patch Size | The size of the square that divides each view into several chunks. Training (feature detection) and processing of each view are executed based on each chunk. Generally, a smaller Patch Size works better for catching small subtle blobs, and a larger Patch Size for large obvious blobs in terms of convergence speed in training.
Stride | The ratio of stride for a Patch when extracting features from a view with Patch. The width-wise pixel stride for a Patch = Patch width x Stride The height-wise pixel stride for a Patch = Patch height x Stride For example, if a Patch's size is 128x128 and Stride is 25% then the steps for width-wise stride and height-wise stride for this Patch are 32 (128x0.25). Generally, a smaller Stride works better in catching the details of an image so that increases the network model accuracy but slows down Train speed given a certain Epoch value. The available value is 10%–100%. Stride of 100% means non-overlapping Patch extraction.