Object Detection Demo Can Handle Multi-class?
cowbaz opened this issue · 2 comments
I am successfully to deploy demo single person class. Then I have trained multi class model using training scripts provided and it can be verified itself, i.e. it can have fair mAP meaning the inferencing is going right. Then I compare the architecture of the demo STM pretrained mobilenet and it is sightly different from the mobilenet created by train.py. I have also tried to upload the .h5 file in AI development cloud for anaylsis of the model. It returns the following exception.
/*
stm32ai validate --model best_model.h5 --allocate-inputs --allocate-outputs --relocatable --compression none --optimization balanced --name network --workspace workspace --output output
Neural Network Tools for STM32 family v1.7.0 (stm.ai v8.1.0-19520)
E010(InvalidModelError): Couldn't load Keras model best_model.h5,
error: Exception encountered when calling layer "lambda_5" (type CustomLambda).
name 'gen_anchors' is not defined
Call arguments received by layer "lambda_5" (type CustomLambda):
• inputs=tf.Tensor(shape=(None, 32, 32, 32), dtype=float32)
• mask=None
• training=None
*/
As the network can be loaded to the board, except nothing can be detected. I am wondering anything else I should config or I need to deal with the model architecture?
Many thanks.
Hello
We have indeed identified an issue with multiclass model deployment and we are investigating. We will come back to you very soon.
Hello,
The issue was solved with the latest updates, the deployment should work now for multi class models.