I trained two different models, and to be honest, I don’t think it’s the Edge impulse.First of all, the two models I trained were completely different. Their trained.tflite sizes were different.
The size of trained.tflite of model A is 625k, and the size of trained.tflite of model B is 56kb. The cause of the problem is that I trained model A on impulse, but to my surprise, there were bugs in the operation,The IDE prompt is" Exception: Failed to load “trained.tflite”, did you copy the .tflite and labels.txt file onto the mass-storage device? (Out of fast Frame Buffer Stack Memory! Please reduce the resolution of the image you are running this algorithm on to bypass this issue!) ".
After two days of debugging and asking for help in others forums , I got very little useful information.I personally found a problem after testing, the argument results are as follows:
I’m running my model B (56kb) because Model B is small so it can run without a memory card
The quiz begins:
I will use model B（56kb） (containing：ei_object_detection.py and labels.txt and trained.tflite）Download it into the tf card, plug it into openmv and run ei_object_detection.py It works fine.
Again, I’m going to take model A (625kb) (containing：ei_object_detection.py and labels.txt and trained.tflite）Download it into the tf card, plug it into openmv and run ei_object_detection.py the IDE report an error like before.
So why will the same operation report an error?And I’ve plugged in the memory card.
And please take note of the above:" I’m running my model B (56kb) because Model B is small so it can run without a memory card "
Therefore, Whether or not it is only possible to run when the size of the model is smaller than the capacity of Openmv without the memory card.If so, what do you need a memory card slot for?
As for the bug problem, it seems that I haven’t found a way to solve my current problem through the previous answers to similar problems of others.
Although the training failure problem in this article does not occur, Openmv’s IDE still reports errors anyway.Hope a good solution.