I am facing a problem about deploying the quantized model on Raspberry Pi.
I build my own model and convert it by the TensorFlow Lite. In this case, how could I deploy this quantized model to Rasberry, or is it possible to deploy my own created model?
Thank for helps!
I was struggling deploying the quantized model on Rasberry Pi, however, some online blogs said must used the pretrained one. SO I was wondering how could I solve this problem.
Related
I build my own custom tensorflow lite model. Now I want to deploy in on my raspberry pi to segment images. Can anyone help me with this problem. How do i proceed to complete this task?
I have been trying to perform image segmentation with raspberry Pi. I searched for different pre-trained models to perform it and came across tensorflow lite, it has a deeplab model in it, it is very less in size (2.7 Mb) and can be used for IOT devices. But in my case, I have a custom dataset and I need to train the model on my dataset (i.e training deeplab with custom dataset). My issue is raspberry Pi has less RAM and storage (comparatively). So, if I train deeplab with the custom dataset, can I run it on raspberry Pi. If so, is there any tutorial or a research paper about it?
You can use this training script. Clone the repository and run model.py from model/research/deeplab.
I wouldn´t train the model on the Raspberry Pi, because it´s damn slow. A better approach would be to train it on a PC (maybe with GPU support) and export the model to the Raspberry Pi.
I'm prototyping mobile ml application within Unity engine.
I have trained tensorflow graph (.pb) and I want to run the model in unity mobile. (both android and ios)
With OpenCVForUnity plugin, with dnn module, I can run tensorflow graph in mobile. But the problem is that's running on CPU.
I need GPU based solution and it seems that OpenCVForUnity isn't proper approach for that.
So any idea for running graph on GPU in unity mobile enviroment?
You might want to use Barracuda, which will allow you to convert a tensorflow model and use it in cross-platform Unity applications. Unity ML-Agents uses Barracuda, so you could use their code as a reference for how to utilize your neural network.
I am trying to train classifier for real time face recognition using IBM Watson in unity,i am unable to do it even after running demo. I am not actually getting where the code or scene starts.can i know how to train classifier and recognize real time face step by step?
Is it possible to run ARCore on Raspberry pi 3? It seems it possible to run Android on Raspberry but it's not clear am I able to run ARCore here.
No, Rasberry pi has no camera and no IMU and even if you would add them yourself, it is highly unlikely that Google would provide a calibration for it.
This calibration is needed to understand how to interprete IMU measurements and camera features and translate them into translation and rotation