Hardware validation of NN model

[Ritesh1]

I am validating a classification model on STM32H745-DISC0 board. 

The validation is successful but id does not show any accuracy related parameter.

It shows all the parameters except accuracy.

 

rmse

Mae

L2r

mean

std

 

 

 

 

Ritesh

[hamitiya]

Hello,

The tool shows accuracy (acc column) means "Classification accuracy". If it is not displayed, it means we were not able to detect your model as a classifier. You can still force this flag with '--classifier' or through the "Advanced Settings" in STM32CubeMX / X-CUBE-AI.

From the embedded documentation:

"

Classification accuracy (acc)

For classifier model type, Classification accuracy is what we usually mean, when the term accuracy is used. ACC is the ratio between of correct predictions to the total number of inputs. This indicator evaluates the performance of the classifier model, if a regressor type is passed, the ACC is NOT calculated and n.a. value is reported

"

 

Best regards,

Yanis

[Ritesh1]

Thanks for the reply.

 

I forced the model as classifier,  but still it does not show the accuracy. 

 

 

 

 

Ritesh

[hamitiya]

Hello,

I assume here you are refering to the validation result in X-CUBE-AI interface in STM32CubeMX.

While doing the validation, could you verify if you have something like these rows in the textual output:

 

Computing the metrics...
 Cross accuracy report #1 (reference vs C-model)
 ----------------------------------------------------------------------------------------------------
 notes: - the output of the reference model is used as ground truth/reference value
 - 10 samples (5 items per sample)
 acc=100.00%, rmse=0.000000153, mae=0.000000065, l2r=0.000000396, nse=1.000, cos=1.000
 5 classes (10 samples)
 ---------------------------------
 C0 0 . . . .
 C1 . 0 . . .
 C2 . . 0 . .
 C3 . . . 8 .
 C4 . . . . 2
 Evaluation report (summary)
 -------------------------------------------------------------------------------------------------------------------------------------------
 Output acc rmse mae l2r mean std nse cos tensor
 -------------------------------------------------------------------------------------------------------------------------------------------
 X-cross #1 100.00% 0.0000002 0.0000001 0.0000004 -0.0000000 0.0000002 1.0000000 1.0000000 activation_3, (5,), m_id=[5]
 -------------------------------------------------------------------------------------------------------------------------------------------
 acc : Classification accuracy (all classes)
 rmse : Root Mean Squared Error
 mae : Mean Absolute Error
 l2r : L2 relative error
 nse : Nash-Sutcliffe efficiency criteria, bigger is better, best=1, range=(-inf, 1]
 cos : COsine Similarity, bigger is better, best=1, range=(0, 1]

 

You should have at least one row (X-cross #<n> where n is the output index).

 

Best regards,

Yanis

[Ritesh1]

How to provide images as a validation input.