Hello, I am trying to use the MLC on the LSM6DSOX with the 6D Position Recognition Sample.The Interrupts on INT1 are triggered as expected, but when I read the MLC0_SRC (0x70U) it always returns 0. Moreover, I tried different samples - same problem.I...
Hello,I have a recurrent error when I try to validate a model using X-CUBE-AI : "TOOL ERROR: Weights are incompatible with model: xxxxx loaded, 89864 found", where xxxxx depends on the model I try to validate. The error occurs in the CubeMX software ...
Hey!After doing some work I connected a Nucleo-L011K4 to the STEVAL-MKI197V1 breakout sensor board with the LSM6DSOX. I am connected through SPI, loaded the configuration (header file) on it and get it running. I've also set up a GPIO on the Nucleo b...
I am looking to create a project where I can set/latch the interrupt pin INT1 on high when the LSM6DSOX is detecting state X (where X is state 1/2/3 etc.) and goes down (low) when in a generic/static state (state 0). How is this possible? I found in ...
I am building CNN model on TensorFlow and after that I generate C code for STM32 micro using CubeMX-AI.Here's my modelmodel = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32,1))) model.add(layers.MaxPool...
Hi, As the title says, I'm trying to use a model with integer weights in cube_ai for speed and size performances. I converted the original keras model to int operations using TFtiny, but when I try to import it, cube_ai throws an error warning me tha...
When I'm trying to analyze the network in CubeMX I get the following error: TOOL ERROR: Unsuportet Layer Type: LSTM. Is there any possible Version of Cube AI wich supports an Recurrent Type Layer such as LSTM or GRU?
Hi, I'm working on a project involving using NN with cube ai to track objects from images coming from a camera sensor. I would like to find a way to downsample an image in the fastest / less cpu intensive way possible (the video feed is approximately...
Hello,I created a CNN model used Keras with tensorflow here's the modelmodel = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32,1))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, ...