How to see an STM32 Prediction output after implementing a pre-trained model ?

[OGaie]

How do you see an STM32 Prediction output after implementing a pre-trained model with MXCUBE and validating it on desktop and on target?

https://www.youtube.com/watch?v=grgNXdkmzzQ&t=

So i accomplished all the validation concerning the HAR CNN Keras Model , how can I see an obvious output of the prediction of the system ?

More explicitly: if i enter a custom data of a person "Walking" how can I see that the system has analyzed the data and predicted that the person is indeed walking...

[Gln]

Hi @OGaie​ ,

When using custom data as validation input, you can get a .csv log file of the output predicted values. The output file location is listed in the X-CUBE-AI log. For example:

Cross accuracy report (reference vs C-model) 
---------------------------------------------------------------------------------------------------- 
NOTE: the output of the reference model is used as ground truth value 
 
acc=100.00%, rmse=0.0003, mae=0.0001 
 
6 classes (12 samples) 
-------------------------------------- 
C0         2    .    .    .    .    .   
C1         .    2    .    .    .    .   
C2         .    .    2    .    .    .   
C3         .    .    .    2    .    .   
C4         .    .    .    .    2    .   
C5         .    .    .    .    .    2   
 
Creating C:\Users\legraing\.stm32cubemx\stm32ai_output\network_val_inputs.csv 
Creating C:\Users\legraing\.stm32cubemx\stm32ai_output\network_val_m_outputs.csv 
Creating C:\Users\legraing\.stm32cubemx\stm32ai_output\network_val_c_outputs.csv 
Creating C:\Users\legraing\.stm32cubemx\stm32ai_output\network_val_io.npz

In X-CUBE-AI v4.0.0, you can also provide some expected output (ground truth) to the tool and a confusion matrix will be shown in the output log.

Output files format is described in X-CUBE-AI v4.0.0, cli documentation, section 4.7 "Post-processing support (generated data files)"

C:/Users/<username>/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/4.0.0/Documentation/an_x_cube_ai_4.0_cli.html

Best regards,

Guillaume

[OGaie]

I'm working on X-CUBE-AI v3.4 and using a pre-trained model based on the cifar10 dataset i have 10 classes here :

0 - airplane

1 - automobile

2 - bird

3 - cat

4 - deer

5 - dog

6 - frog

7 - horse

8 - ship

9 - truck

all I can see in the ai_outputs_model.csv and the ai_stm32_msg.log is this :

PS: i used a custom data for cat pictures

[Gln]

Hi @OGaie​ ,

Great! the ai_outputs_models.csv is the file you should be looking at.

In your spreadsheet, you can see you have 7 rows and 10 columns. Each row correspond to the output of an inference. I'm guessing that the last layer of your classifier model is a SoftMax activation function, in that case, you are getting the probability distribution of your 10 classes.

E.g., for the first row:

7.43e-07, 2.60e-28, ..., 9.99e-01, ..., 1.08e-20 -> index n#6 has the highest value (0.999). The first prediction output is a "6 - frog".

Best regards,

Guillaume

[OGaie]

the data shape accepted in the custom data input in the MXCube for this model is 32*32*3

so I tired to enter the data of a "cat"

cat = {226,221,195,247,241,227,246,239,227,243,237,225,243,237,222,243,236,220,241,234,218,239,229,213,237,226,210,235,224,207,234,226,208,238,229,213,237,204,192,217,142,137,........,210,194,211,198,182}

and the prediction is always wrong ...

i tried to normalize the data in range of [-1,1] and its still wrong so i'm asking why?

and how can I enter a custom data easily in the shape demanded my mxcube model without going through python codes , arrays and conversions ...etc , just to create a custom data

[RESEN.1]

Hi OGaie

I'm need an one dimensional artificial intelligence project with Stm32 so I am need to run this project.

I watched this video too but I don't know how to continue later. how did you manage to get an output like the one above?

I would be glad if you tell me step by step.

Regards

[OGaie]

Using the MX Cube AI 4.0 you can see the confusion matrix in the validation on target , so first u need to configure your usart instance and run the cube ai package as validation and load it on your board , then u validate on target (in the mx cube ai interface provided ) using custom data to see if the model is working correctly

[RESEN.1]

Thanks for your answer

How can I fix the error in this picture?

[OGaie]

Check your cable if its connected or not and if you are using the usart configuration correctly?

you need to enable the usart from the interface too

[RESEN.1]

I checked the cable, it is working properly.

Usart settings in the below. Do I need to make any other setting related to usart?