This article describes how to measure the performances of a Coral Edge TPU neural network model on STM32MP1x plateform.
1. Installation[edit source]
1.1. Install from the OpenSTLinux AI package repository[edit source]
After having configured the AI OpenSTLinux package you can install X-LINUX-AI components for this application. The minimum package required is :
apt-get install tflite-edgetpu-benchmark
The model used in this example can be installed from the following package:
apt-get install tflite-models-coco-ssd-mobilenetv1-edgetpu
2. How to use the Benchmark application[edit source]
2.1. Executing with the command line[edit source]
The tflite_edgetpu_benchmark application is located in the userfs partition:
/usr/local/bin/demo-ai/benchmark
It accepts the following input parameters:
Usage: ./tflite-edgetpu-benchmark -m --model_file <.tflite file path>: .tflite model to be executed -l --loops <int>: provide the number of time the inference will be executed (by default nb_loops=1) --help: show this help
2.2. Testing with COCO SSD MobileNet V1[edit source]
The model used for testing is the detect_edgetpu.tflite which is a COCO SSD MobilenetV1. It is a model used for object detection.
On the target, the model is located here:
/usr/local/demo-ai/computer-vision/models/coco_ssd_mobilenet/
To launch the application, use the following command :
./tflite-edgetpu-benchmark -m <model .tflite> -l <number of loops>
In ouput, this benchmark script will return the following line:
inference time: min=65734us max=77319us avg=74377.3us
With that, you can have an idea of your model performances.