**Question/Issue: Is there any way to train a model to get a Tensorflow Lite model that can be used for non-Edge Impulse TensorflowLite? TensorFlow lite converter? Or do i have no choice but to retrain using Tensorflow Lite google colab?
Hi,i am very new to Tensorflow Lite. I’m currently working on an object detection project on my Raspberry Pi 4, following the guide here (https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/deploy_guides/Raspberry_Pi_Guide.md) and all is working fine.
Now, I have trained a few models that I have previously used on my ESP32-Cam and would like to use the same models for my Raspberry PI. I have used the Lite file on the dashboard and renamed them tflite but there were some issues getting them to work. I have tried the ones u can get from the deployment tab “OpenMV Library” but its not working as well. For the dashboard files:
Using the quantized int8 model, the error shows “ValueError: Cannot set tensor: Got value of type UINT8 but expected INT8 for input 0, name: serving_default_x:0”
While if I were to use the float32 model, the error shows to be Bounding box coordinates of detected objects IndexError: List index out of range.