# High Detail Mode Resizing Parameter

Resizing parameter is for resizing views in High Detail mode. This parameter option is only available in expert mode.

High Detail mode cannot train oversized views, and the maximum buffer size is decided by available GPU memory and the network size option. The available GPU memory means the value of (total GPU memory of computing device – 2.2GB buffer memory). If there are views larger than the maximum buffer size, the tool resizes the view before training.

The maximum buffer size is automatically calculated based on available GPU memory and the network model option.

- If all views in the tool have an identical aspect-ratio, the maximum buffer size will be calculated with the aspect-ratio of the views.
- If not, the maximum buffer size will be calculated with 1:1 aspect-ratio.

Maximum Buffer Size by GPU Memory

GRAM | Network Model
--- | ---
Small | Normal | Large | Extra Large
6GB | 764x764 | 960x960 | 564x564 | 376x376
8GB | 944x944 | 1164x1164 | 696x696 | 464x464
11GB | 1164x1164 | 1432x1432 | 860x860 | 572x572

Resize Mode

You can resize the view in two ways: auto mode and manual mode. In both methods, resizing is applied to the entire views in the High Detail mode. Also, it only resizes the view which is used during training, but the original images are not resized at all.

Tip:

- Resizing causes loss of image information for training. To avoid information loss, please adjust ROI area to reduce the size of views for training. (High Detail mode image restriction depends on ROI size)
- If you use High Detail mode with oversized view in non-expert mode, the tool will automatically execute a resizing in auto mode.

If you train the model with big images than the current GPU capacity, you can have relatively lower performance with GPUs with smaller GRAM.

- You can check the maximum size in expert mode resize tool.
- If you are using other software that consumes lots of GRAM, the maximum size to train could be shrinked lower than guided amount.

This affects to the performance of the model since the size of input image is getting smaller when the GRAM size is smaller. The critical clues could be gone if you resize the image to extremely small size.