Macros

The following list gives information about the most important #define macros found in model-parameters/model_metadata.h. Note that not all macros are listed—just the ones you’ll probably care about. Source: Can be found in model-parameters/model_metadata.h if you deploy your impulse as a C++ library.

Important! model_metadata.h is automatically generated by the Edge Impulse Studio. You should not modify it.

Macro	Description
`EI_CLASSIFIER_NN_INPUT_FRAME_SIZE`	Number of inputs (words) to the machine learning model block. Should match the number of outputs of the preprocessing (DSP) block. For example, if the DSP block outputs a 320x320 image with 1 word for each color (RGB), then `EI_CLASSIFIER_NN_INPUT_FRAME_SIZE` will be 320 * 320 * 3 = 307200. The trained machine learning model will expect this number of inputs.
`EI_CLASSIFIER_RAW_SAMPLE_COUNT`	Number of sample frames expected by the DSP block. For example, if your window size is set to 2000 ms with a 100 Hz sampling rate, `EI_CLASSIFIER_RAW_SAMPLE_COUNT` will equal 2 s * 100 Hz = 200 sample frames. For image data, this is the total number of pixels in the input image, which is equal to `EI_CLASSIFIER_INPUT_WIDTH * EI_CLASSIFIER_INPUT_HEIGHT`.
`EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME`	Number of numerical samples in each frame. For example, if you are using a 3-axis accelerometer, `EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME` is 3.
`EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE`	Total number of values expected by the DSP block input. It is equal to `EI_CLASSIFIER_RAW_SAMPLE_COUNT * EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME`.
`EI_CLASSIFIER_INPUT_WIDTH`	Image data will be resized so that the width matches this amount, using the Resize mode method set in the Edge Impulse Studio. Set to 0 for non-image data.
`EI_CLASSIFIER_INPUT_HEIGHT`	Image data will be resized so that the height matches this amount, using the Resize mode method set in the Edge Impulse Studio. Set to 0 for non-image data.
`EI_CLASSIFIER_INPUT_FRAMES`	Number of image frames used as input to an impulse. Set to 1 for image classification and object detection tasks. Set to 0 for non-image data.
`EI_CLASSIFIER_INTERVAL_MS`	Number of milliseconds between sampling the sensor. For non-image data, this is equal to `1000 / EI_CLASSIFIER_FREQUENCY`. Set to 1 for image data.
`EI_CLASSIFIER_LABEL_COUNT`	Number of labels in `ei_classifier_inferencing_categories[]`, which is the number of classes that can be predicted by the classification model.
`EI_CLASSIFIER_HAS_ANOMALY`	Set to 1 if there is an anomaly block in the impulse, 0 otherwise.
`EI_CLASSIFIER_FREQUENCY`	Sampling frequency of the sensor(s). For non-image data, this is equal to `1000 / EI_CLASSIFIER_INTERVAL_MS`. Set to 0 for image data.
`EI_CLASSIFIER_HAS_MODEL_VARIABLES`	Set to 1 if model-parameters/model_variables.h is present in the library, 0 otherwise.
`EI_CLASSIFIER_OBJECT_DETECTION`	Set to 1 if the impulse is configured for object detection, 0 otherwise.
`EI_CLASSIFIER_OBJECT_DETECTION_COUNT`	If `EI_CLASSIFIER_OBJECT_DETECTION` is set to 1, this macro is defined. Maximum number of objects that will be detected in each input image.
`EI_CLASSIFIER_OBJECT_DETECTION_THRESHOLD`	If `EI_CLASSIFIER_OBJECT_DETECTION` is set to 1, this macro is defined. Only bounding boxes with confidence scores equal to or above this value will be returned from inference.
`EI_CLASSIFIER_OBJECT_DETECTION_CONSTRAINED`	If `EI_CLASSIFIER_OBJECT_DETECTION` is set to 1, this macro is defined. Set to 1 if constrained object detection model is used, 0 otherwise.
`EI_CLASSIFIER_INFERENCING_ENGINE`	The inferencing engine to be used. This can have the following values. Default is `EI_CLASSIFIER_TFLITE`, which uses TensorFlow Lite for Microcontrollers (TFLM) as the inference engine. `EI_CLASSIFIER_NONE` `EI_CLASSIFIER_UTENSOR` `EI_CLASSIFIER_TFLITE` `EI_CLASSIFIER_CUBEAI` `EI_CLASSIFIER_TFLITE_FULL` `EI_CLASSIFIER_TENSAIFLOW` `EI_CLASSIFIER_TENSORRT`
`EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW`	Number of slices to gather per window. For example, if you want `run_classifier_continuous()` to be called every 0.25 s and you have a window size of 1 s, `EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW` should be set to 4. It is set to 4 by default. Note that you can override this value in your main code if you `#define` this macro prior to including the SDK. For example: `#define EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW 3 #include “edge-impulse-sdk/classifier/ei_run_classifier.h”` See this guide to learn more about continuous sampling. You can see an example here that shows how to set the number of slices per window to something other than 4 prior to including the Edge Impulse C++ SDK library.
`EI_CLASSIFIER_SLICE_SIZE`	Number of samples in a slice. Equal to `EI_CLASSIFIER_RAW_SAMPLE_COUNT / EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW`. For `run_classifier_continouous()` applications, you can usually set `signal.total_length` to `EI_CLASSIFIER_SLICE_SIZE`. See this example.
`EI_CLASSIFIER_USE_FULL_TFLITE`	This can be defined and set to 1 by the user if using full TensorFlow Lite. Note that setting this to 1 while `EI_CLASSIFIER_INFERENCING_ENGINE` is set to `EI_CLASSIFIER_TFLITE` will force `EI_CLASSIFIER_INFERENCING_ENGINE` to be set to `EI_CLASSIFIER_TFLITE_FULL`. Not compatible with EON Compiler.

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