You can modify parameters and specify additional parameters to achieve more precise calibration and accuracy results. You are required to specify several parameters for classification and object-detection models.
To configure accuracy settings, click on the gear sign next to the model name in the Projects table or go to the settings from the Int-8 quantization tab before the optimization process. Once you have specified your parameters, you are directed back to your previous window, either the Projects table or the Int-8 tab.
Accuracy settings depend on the model usage. The default usage of a model is Generic. Specify Classification, Object-Detection or Generic usage in the drop-down list in the Accuracy Settings. If you choose Object Detection, specify SSD or YOLO model type. The latter requires additional type specification between V2 and Tiny V2:
Refer to the table below to see available parameters for each usage type:
Usage | Configuration Parameters |
---|---|
Classification | Preprocessing configuration: Separate background class, Normalization Metric configuration: Metric, Top K |
Object-Detection SSD | Preprocessing configuration: Resize type, Color space, Separate background class, Normalization Post-processing configuration: Prediction boxes Metric configuration: Metric (Auto), Overlap threshold, Integral |
Object-Detection YOLO V2 and YOLO Tiny V2 | Preprocessing configuration: Resize type, Color space, Separate background class, Normalization Post-processing configuration: Prediction boxes, NMS overlap Metric configuration: Metric (Auto), Overlap threshold, Integral |
Generic | No parameters required |
Do not change optional settings unless you are well aware of the impact they have. For more details on parameter setting for calibration and accuracy checking, refer to the command-line documentation.
Defines how to process images prior to inference with a model.
Parameter | Values | Explanation |
---|---|---|
Resize Type | Auto | Resize images to the model input dimensions |
Color Space | RGB BGR |
Transform image color space from RGB to BGR or back |
Separate Background Class | Yes No |
Insert background label as an additional label for all images |
Normalization: Mean | [0; 256] | The values to be subtracted from the corresponding image channels |
Normalization: Standard Deviation | [0; 256] | The values to divide image channels by |
Defines how to process images after inference with a model. Post-processing also provides prediction values and/or annotation data after inference and before metric calculation.
Parameter | Values | Explanation |
---|---|---|
Prediction Boxes | None ResizeBoxes ResizeBoxes NMS |
Resize images or set Non-Maximum Suppression (NMS) to make sure that detected objects are identified only once |
NMS Overlap | [0; 1] | Non-maximum suppression overlap threshold to merge detections |
Specifies a metric for post-inference measurements.
Parameter | Values | Explanation |
---|---|---|
Metric | Auto | The unit of measurement applied to evaluate performance of a model |
Overlap Threshold | [0; 1] | Minimal value for intersection over union to qualify that a bounding box of a prediction as true positive |
Integral | Max 11 Point |
Integral type to calculate average precision |