Struct ModelParameters

pub struct ModelParameters {

    pub axis_count: u32,
    pub frequency: f32,
    pub has_anomaly: RunnerHelloHasAnomaly,
    pub has_object_tracking: bool,
    pub image_channel_count: u32,
    pub image_input_frames: u32,
    pub image_input_height: u32,
    pub image_input_width: u32,
    pub image_resize_mode: String,
    pub inferencing_engine: u32,
    pub input_features_count: u32,
    pub interval_ms: f32,
    pub label_count: u32,
    pub labels: Vec<String>,
    pub model_type: String,
    pub sensor: i32,
    pub slice_size: u32,
    pub thresholds: Vec<ModelThreshold>,
    pub use_continuous_mode: bool,
}

Parameters that define a model’s configuration and capabilities.

These parameters are received from the model during initialization and describe the model’s input requirements, processing settings, and output characteristics.

Fields

axis_count: u32

Number of axes for motion/positional data (e.g., 3 for xyz accelerometer)

frequency: f32

Sampling frequency in Hz for time-series data

has_anomaly: RunnerHelloHasAnomaly

Indicates if the model supports anomaly detection

has_object_tracking: bool

Indicates if the model supports object tracking (0 = no, 1 = yes)

image_channel_count: u32

Number of color channels in input images (1 = grayscale, 3 = RGB)

image_input_frames: u32

Number of consecutive frames required for video input

image_input_height: u32

Required height of input images in pixels

image_input_width: u32

Required width of input images in pixels

image_resize_mode: String

Method used to resize input images (“fit” or “fill”)

inferencing_engine: u32

Type of inferencing engine (0 = TensorFlow Lite, 1 = TensorFlow.js)

input_features_count: u32

Total number of input features expected by the model

interval_ms: f32

Time interval between samples in milliseconds

label_count: u32

Number of classification labels

labels: Vec<String>

Vector of classification labels

model_type: String

Type of model (“classification”, “object-detection”, etc.)

sensor: i32

Type of input sensor (see SensorType enum)

slice_size: u32

Size of the processing window for time-series data

thresholds: Vec<ModelThreshold>

Vector of thresholds for different types of detections

use_continuous_mode: bool

Whether the model supports continuous mode operation

Trait Implementations

impl Clone for ModelParameters

fn clone(&self) -> ModelParameters

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ModelParameters

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ModelParameters

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for ModelParameters

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.