MCUXpresso_MIMXRT1052xxxxB/boards/evkbimxrt1050/eiq_examples/mpp_camera_mobilenet_view_tflm

Overview

This example shows how to use the library to create a use-case for image classification using camera as source.

The machine learning frameworks used are TensorFlow Lite Micro, GLOW or DeepViewRT The image classification model used is quantized Mobilenet convolutional neural network model [1] that classifies the input image into one of 1000 output classes.

[1] https://www.tensorflow.org/lite/models

Toolchains supported

• MCUXpresso, version 11.8.0
• GCC Arm Embedded, version 12.2.Rel1

SDK version

• Version: 2.15.000

Toolchain supported

• GCC ARM Embedded 12.2
• MCUXpresso 11.8.0

Hardware requirements

• Mini/micro USB cable
• EVKB-IMXRT1050 board
• Personal computer
• MT9M114 camera (optional)
• RK043FN02H-CT display (optional)

Board settings

Connect the camera to J35 (optional) Connect the display to A1-A40 and B1-B6 (optional) Connect external 5V power supply to J2, set J1 to 1-2

Prepare the Demo

1. Connect a USB cable between the host PC and the OpenSDA USB port on the target board.
2. Open a serial terminal with the following settings:
   • 115200 baud rate
   • 8 data bits
   • No parity
   • One stop bit
   • No flow control
3. Download the program to the target board.
4. Either press the reset button on your board or launch the debugger in your IDE to begin running the demo.

Running the demo

EXPECTED OUTPUTS: The expected outputs of the example are:

• Detected label should be displayed on the screen
• Pipeline tasks statistics are displayed on the console
• Logs below should be displayed on the debug console

Logs for camera_mobilenet_view example using TensorFlow Lite Micro model should look like this:

[MPP_VERSION_1.0.0] Inference Engine: TensorFlow-Lite Micro API stats ------------------------------ rc_cycle = 43 ms rc_cycle_max 99 ms pr_slot = 56 ms pr_rounds 1 app_slot 4 ms MPP stats ------------------------------ mpp 80082C40 exec_time 43 ms mpp 80082F20 exec_time 52 ms Element stats -------------------------- mobilenet : exec_time 48 ms mobilenet : No label detected (0%) API stats ------------------------------ rc_cycle = 43 ms rc_cycle_max 99 ms pr_slot = 56 ms pr_rounds 1 app_slot 4 ms MPP stats ------------------------------ mpp 80082C40 exec_time 43 ms mpp 80082F20 exec_time 52 ms Element stats -------------------------- mobilenet : exec_time 49 ms mobilenet : mosquito net (32%) API stats ------------------------------ rc_cycle = 43 ms rc_cycle_max 99 ms pr_slot = 56 ms pr_rounds 1 app_slot 4 ms MPP stats ------------------------------ mpp 80082C40 exec_time 43 ms mpp 80082F20 exec_time 52 ms

Important notes

TensorFLow Lite Micro is an optional engine for the ML Inference component of MPP. This project embeds NXP's custom TensorFlow Lite Micro code by default. TensorFLow Lite allows short-listing the "Operations" used by a specific model in order to reduce the binary image footprint. This is done by implementing the function:

tflite::MicroOpResolver &MODEL_GetOpsResolver()

If this example uses TensorFLow Lite Micro (i.e. not Glow, not deepViewRT), it implements its own function MODEL_GetOpsResolver dedicated to Mobilenet. User may provide its own implementation of MODEL_GetOpsResolver when using a different model.