# Model configuration

Model's static method `create_model()` has two overloads. One constructs the model from a string (a path or a model name) and the other takes an already constructed `InferenceAdapter`. The first overload configures a created model with values taken from `configuration` dict function argument and from model's intermediate representation (IR) stored in `.xml` in `model_info` section of `rt_info`. Values provided in `configuration` have priority over values in IR `rt_info`. If no value is specified in `configuration` nor in `rt_info` the default value for a model wrapper is used. For Python configuration values are accessible as model wrapper member fields.

## List of values

The list features only model wrappers which introduce new configuration values in their hierarchy.

1. `model_type`: str - name of a model wrapper to be created
1. `layout`: str - layout of input data in the format: "input0:NCHW,input1:NC"

### `ImageModel` and its subclasses

1. `mean_values`: List - normalization values, which will be subtracted from image channels for image-input layer during preprocessing
1. `scale_values`: List - normalization values, which will divide the image channels for image-input layer
1. `reverse_input_channels`: bool - reverse the input channel order
1. `resize_type`: str - crop, standard, fit_to_window or fit_to_window_letterbox
1. `embedded_processing`: bool - flag that pre/postprocessing embedded
1. `pad_value`: int - pad value for resize_image_letterbox embedded into a model

#### `AnomalyDetection`

1. `image_shape`: List - Input shape of the model
1. `image_threshold`: float - Image threshold that is used for classifying an image as anomalous
1. `pixel_threshold`: float - Pixel level threshold used to segment anomalous regions in the image
1. `normalization_scale`: float - Scale by which the outputs are divided. Used to apply min-max normalization
1. `task`: str - Outputs segmentation masks, bounding boxes, or anomaly score based on the task type

#### `ClassificationModel`

1. `topk`: int - number of most likely labels
1. `labels`: List - list of class labels
1. `path_to_labels`: str - path to file with labels. Overrides the labels, if they sets via 'labels' parameter
1. `multilabel`: bool - predict a set of labels per image
1. `hierarchical`: bool - predict a hierarchy of labels per image hierarchical_config
1. `confidence_threshold`: float - probability threshold value for multilabel or hierarchical predictions filtering
1. `hierarchical_config`: str - a serialized configuration for decoding hierarchical predictions
1. `output_raw_scores`: bool - output all scores for multiclass classification

#### `DetectionModel` and its subclasses

1. `confidence_threshold`: float - probability threshold value for bounding box filtering
1. `labels`: List - List of class labels
1. `path_to_labels`: str - path to file with labels. Overrides the labels, if they sets via `labels` parameter

##### `CTPN`

1. `iou_threshold`: float - threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering
1. `input_size`: List - image resolution which is going to be processed. Reshapes network to match a given size

##### `FaceBoxes`

1. `iou_threshold`: float - threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering

##### `NanoDet`

1. `iou_threshold`: float - threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering
1. `num_classes`: int - number of classes

##### `UltraLightweightFaceDetection`

1. `iou_threshold`: float - threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering

##### `YOLO` and its subclasses

1. `iou_threshold`: float - threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering

###### `YoloV4`

1. `anchors`: List - list of custom anchor values
1. `masks`: List - list of mask, applied to anchors for each output layer

###### `YOLOv5`, `YOLOv8`

1. `agnostic_nms`: bool - if True, the model is agnostic to the number of classes, and all classes are considered as one
1. `iou_threshold`: float - threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering

###### `YOLOX`

1. `iou_threshold`: float - threshold for non-maximum suppression (NMS) intersection over union (IOU) filtering

#### `HpeAssociativeEmbedding`

1. `target_size`: int - image resolution which is going to be processed. Reshapes network to match a given size
1. `aspect_ratio`: float - image aspect ratio which is going to be processed. Reshapes network to match a given size
1. `confidence_threshold`: float - pose confidence threshold
1. `delta`: float
1. `size_divisor`: int - width and height of the reshaped model will be a multiple of this value
1. `padding_mode`: str - center or right_bottom

#### `OpenPose`

1. `target_size`: int - image resolution which is going to be processed. Reshapes network to match a given size
1. `aspect_ratio`: float - image aspect ratio which is going to be processed. Reshapes network to match a given size
1. `confidence_threshold`: float - pose confidence threshold
1. `upsample_ratio`: int - upsample ratio of a model backbone
1. `size_divisor`: int - width and height of the reshaped model will be a multiple of this value

#### `MaskRCNNModel`

1. `confidence_threshold`: float - probability threshold value for bounding box filtering
1. `labels`: List - list of class labels
1. `path_to_labels`: str - path to file with labels. Overrides the labels, if they sets via `labels` parameter
1. `postprocess_semantic_masks`: bool - resize and apply 0.5 threshold to instance segmentation masks

#### `SegmentationModel` and its subclasses

1. `labels`: List - list of class labels
1. `path_to_labels`: str - path to file with labels. Overrides the labels, if they sets via 'labels' parameter
1. `blur_strength`: int - blurring kernel size. -1 value means no blurring and no soft_threshold
1. `soft_threshold`: float - probability threshold value for bounding box filtering. inf value means no blurring and no soft_threshold
1. `return_soft_prediction`: bool - return raw resized model prediction in addition to processed one

### `ActionClassificationModel`

1. `labels`: List - list of class labels
1. `path_to_labels`: str - path to file with labels. Overrides the labels, if they sets via 'labels' parameter
1. `mean_values`: List - normalization values, which will be subtracted from image channels for image-input layer during preprocessing
1. `pad_value`: int - pad value for resize_image_letterbox embedded into a model
1. `resize_type`: str - crop, standard, fit_to_window or fit_to_window_letterbox
1. `reverse_input_channels`: bool - reverse the input channel order
1. `scale_values`: List - normalization values, which will divide the image channels for image-input layer

> **NOTE** `ActionClassificationModel` isn't subclass of ImageModel.

### `Bert` and its subclasses

1. `vocab`: Dict - mapping from string token to int
1. `input_names`: str - comma-separated names of input layers
1. `enable_padding`: bool - should be input sequence padded to max sequence len or not

#### `BertQuestionAnswering`

1. `output_names`: str - comma-separated names of output layers
1. `max_answer_token_num`: int
1. `squad_ver`: str - SQuAD dataset version used for training. Affects postprocessing

> **NOTE**: OTX `AnomalyBase` model wrapper adds `image_threshold`, `pixel_threshold`, `min`, `max`, `threshold`.