> ## Documentation Index > Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt > Use this file to discover all available pages before exploring further. # 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_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](/tutorials/topics/inference/sample-audio-continuously) to learn more about continuous sampling. You can see an example [here](https://github.com/edgeimpulse/firmware-eta-compute-ecm3532/blob/08213c4a65b2b74e66b39dac6b29d0af6d8f1682/Applications/edge-impulse-ingestion/src/ei_run_impulse.cpp#L24) 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](https://github.com/edgeimpulse/example-lacuna-ls200/blob/0ab2585e1b5291097a68b5036078e2f6fcd7c03a/nano_ble33_sense_microphone_continous/nano_ble33_sense_microphone_continuous.ino#L94). | | `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. |