classification

Image classification

Overview

Name	Implementation	Platforms	Model	ML engine	Backend	Features
example_classification_mobilenet_v1_tflite.cpp	C++	i.MX 8M Plus i.MX 93 i.MX 95	mobilenet_v1	TFLite	NPU (default) GPU CPU	camera gst-launch
example_classification_two_cameras_tflite.cpp	C++	i.MX 8M Plus i.MX 93 i.MX 95	mobilenet_v1	TFLite	NPU (default) GPU CPU	camera gst-launch
example_classification_mobilenet_v1_tflite.sh	Bash	i.MX 8M Plus i.MX 93 i.MX 95	mobilenet_v1	TFLite	NPU (default) GPU CPU	camera gst-launch

NOTES:

• Warmup time for NPU inference on i.MX 95 can take up to 1 minute
• No GPU support on i.MX 93

Execution

Example script can be called from target console with no further restriction. For examples that support multiple backend, default value can be overriden by explicitly defining BACKEND variable, for instance:

Bash

$ BACKEND=CPU ./classification/example_classification_mobilenet_v1_tflite.sh

C++

C++ example script needs to be generated with cross compilation. setup_environment.sh script needs to be executed in nxp-nnstreamer-examples folder to define data paths:

$ . ./tools/setup_environment.sh

It is possible to run the classification demo inference on three different hardwares:
Inference on NPU with the following script:

$ ./build/classification/example_classification_mobilenet_v1_tflite -p ${MOBILENETV1_QUANT} -l ${MOBILENETV1_LABELS}

For i.MX 93 use vela converted model:

$ ./build/classification/example_classification_mobilenet_v1_tflite -p ${MOBILENETV1_QUANT_VELA} -l ${MOBILENETV1_LABELS}

Inference on CPU with the following script:

$ ./build/classification/example_classification_mobilenet_v1_tflite -p ${MOBILENETV1_QUANT} -l ${MOBILENETV1_LABELS} -b CPU

Quantized model is used for better inference performances on CPU.
NOTE: inferences on i.MX8MPlus GPU have low performances, but are possible with the following script:

$ ./build/classification/example_classification_mobilenet_v1_tflite -p ${MOBILENETV1} -l ${MOBILENETV1_LABELS} -b GPU -n centeredReduced

Input normalization needs to be specified, here input data needs to be centered and reduced to fit MobileNetv1 input specifications.

The following execution parameters are available (Run ./example_classification_mobilenet_v1_tflite -h to see option details):

Option	Description
-b, --backend	Use the selected backend (CPU, GPU, NPU) default: NPU
-n, --normalization	Use the selected normalization (none, centered, reduced, centeredReduced, castInt32, castuInt8) default: none
-c, --camera_device	Use the selected camera device (/dev/video{number}) default: /dev/video0 for i.MX 93 and /dev/video3 for i.MX 8MP
-p, --model_path	Use the selected model path
-l, --labels_path	Use the selected labels path
-d, --display_perf	Display performances, can specify time or freq
-t, --text_color	Color of performances displayed, can choose between red, green, blue, and black (white by default)
-g, --graph_path	Path to store the result of the OpenVX graph compilation (only for i.MX8MPlus)

Press Esc or ctrl+C to stop the execution of the pipeline.

An example using two cameras is available. To NPU backend on on both camera inference, use the following command:
NOTES:

• it is not recommanded to use CPU or GPU bachend because of low performances
• performances may depend on the USB cameras used, especially on i.MX 93

$ ./build/classification/example_classification_two_cameras_tflite -p ${MOBILENETV1_QUANT},${MOBILENETV1_QUANT} -l ${MOBILENETV1_LABELS} -c ${CAM1_PATH},${CAM2_PATH}

For i.MX 93 use vela converted model:

$ ./build/classification/example_classification_two_cameras_tflite -p ${MOBILENETV1_QUANT_VELA},${MOBILENETV1_QUANT_VELA} -l ${MOBILENETV1_LABELS} -c ${CAM1_PATH},${CAM2_PATH}

The following execution parameters are available (Run ./example_classification_two_cameras_tflite -h to see option details):

Option	Description
-b, --backend CAM1_BACKEND,CAM2_BACKEND	Use the selected backend (CPU, GPU, NPU) default: NPU
-n, --normalization CAM1_NORM,CAM2_NORM	Use the selected normalization (none, centered, reduced, centeredReduced, castInt32, castuInt8) default: none
-c, --camera_device CAMERA1,CAMERA2	Use the selected camera device (/dev/video{number}) default: /dev/video0 for i.MX 93 and /dev/video3 for i.MX 8MP
-p, --model_path CAM1_MODEL,CAM2_MODEL	Use the selected model path
-l, --labels_path CAM1_LABELS,CAM2_LABELS	Use the selected labels path
-d, --display_perf	Display performances, can specify time or freq
-t, --text_color	Color of performances displayed, can choose between red, green, blue, and black (white by default)
-g, --graph_path	Path to store the result of the OpenVX graph compilation (only for i.MX8MPlus)

Press Esc or ctrl+C to stop the execution of the pipeline.