I’m using your M7 to run CNNs. Unfortunately I noticed that the performance of the models is not the same as during the test phase. For example, I trained a person recognition (binary classification (person in picture or not)) on the COCO database. The accuracy on the evaluation set is ~80%. As input I use color images of 128x128x3. As network I use a CNN with 4 layers. After quantization I loaded the model to the M7 and executed it. In very few cases the model recognizes people in the camera image. I.e. the recognition rate is bad.
I did the following experiments to find the error: First I loaded 100 images of people from the COCO database onto the microcontroller and processed them one after the other through the model. Of these 100 persons only 11 were recognized.
As a further test I used the quantized caffe model and wrote a small demo application. The pictures taken by my webcam serve as input. The recognition rate of this demo application is subjectively perceived much better. So there seems to be an error during the transformation from the quantized caffe model to the CMSIS code. Is there anything known? Any ideas where the error could be?