I´ve trained a smalled detection dataset using impulse edge as shown in one of your youtube videos. Currently it only has two features, pedestrians and bikes. The model looks very promising but when I save the trained.tflite and lablels.txt in the cam and the execute the tf_face_recognition_1-py example I get the following error: “OSError: Failed to allocate memory. Requested 864, available 464, missing: 400”
You comment that the OpenMVH7, which is the one I am currently using, has arround 400 Kbytes for the binary size, I am guessing it has 464 kbytes. When I trained the network, in the transfer learning window I got a peak RAM usage of 270,3K and a ROM usage of 214,8K. I selected MobileNetV2 0.1 (final layer: 10 neurons, 0.1 dropout) because the page claims that the model is around 270K in size with default settings and optimizations. One pasted into the cam memory, it only takes 295K, so why am I getting the error?
Is because the program + the .tfflite file + the labels.txt file occupy 864K? If that´s the case, I´ve read that adding an SD card won´t make any difference because the constraint is the memory of the board itself, so, should I get the OpenMVH7PLUS in order to be able to use the nwtwork I trained?
I attach a screenshot of the Edge Impulse window and my tflite and labels files.
Thank you for your time and attention,
ei-heroofmonkeys-project-1-openmv-v0.zip (163 KB)