# Red Analyze Tool High Detail Mode Processing Parameters

The Processing parameters control the way that images are processed by Red Analyze Tool in High Detail mode. This is often called ‘inference’ in deep learning. Processing with the same models will always give you the same results. Changing these parameters does not require the tool to be retrained; the effect can be seen right away by reprocessing the database.

Parameters | Description
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Threshold | There are two settings, T1 and T2 (expressed as [T1,T2]. They determine the threshold which determines whether or not regions are detected and marked as good or bad. Values below T1 will be classified as good, and values above T2 will be classified as bad. The T1 and T2 values can also be set interactively using the Scores graphic in the Database Overview.
Auto | When you enable Auto (Auto-Threshold), it calculates Threshold values T1 and T2 that maximize the F1 score of confusion matrix on Database Overview by following each criterion in the dropdown menu. The 4 criteria are the same as the ones in Count dropdown menu on Database Overview. See Count Filter for more information.
Region Filter | Specifies a filter for the tool to be used as criteria for found regions. By specifying a filter, regions that do not match the filter will be removed from the results. If the parameter is left blank, all regions will be returned. Note: The syntax for filters is the same as that used for Display Filters. For more information about constructing the syntax for a filter, see the Custom Display Filters. The available region properties are: score area perimeter compactness x y
Downsampling Size | The magnitude of downsampling. The result of the processing, which is a heatmap that consists of defect probabilities for the input view, is downsampled with a kernel whose size is of this level. For example, if the size of the result is 128x128 with Downsampling Size of 16, Red Analyze High Detail Mode downsamples this with a 16x16 kernel. In this case, the downsampled result becomes an 8x8 patch that contains pixels of the highest defect probabilities in the original a 128x128 output. Then Red Analyze High Detail Mode reconstructs this 8x8 patch into 128x128 heatmap which is the final result. Generally the higher Downsampling Size gives out the faster processing time with the increased recall but some loss in the precision. The available value for Downsampling Size is from 1 to the size of view (the width or the height of view). Changing this value requires more time for re-processing compared to changing other processing parameters, accompanied by a slight change of the processing result due to randomness. Note: Depending on your images, increasing Downsampling Size might result in a decrease in Process Speed. This is natural since increasing the size of the downsampling kernel decreases the amount of blob calculation in Processing but adds more image compressions at the same time.