Image classification (persons, animals, other) on raspberry pi from pi-camera (in live time) using custom model tflite (output to terminal), using TensorFlow Lite
Note before you start:

So, let's start :)

Hardware preparation:

Software preparation:

1. Create a neural network model to predict 3 classes: persons, animals, others.

I have already done it in the article below. So in this article, you do not need to do this.

2 Convert h5 model to tflite model

I have already done it in the article below. So in this article, you do not need to do this.

3 So, let’s try to predict class from camera raspberry pi in live time with output to the terminal using Tensorflow Lite and custom model tflite (which I trained and saved above).

For that, you need to run the next code:



git clone #clone my repogit clone https://github.com/oleksandr-g-rock/image_classification_pi_camera_live_time_custom_model_tflite.git #go to folder

cd image_classification_pi_camera_live_time_custom_model_tflite

wget #download tflite modelwget https://github.com/oleksandr-g-rock/how_to_convert_h5_model_to_tflite/raw/main/animall_person_other_v2_fine_tuned.tflite #run script

python3 classify_picamera_with_live_time_custom_tflite_model.py

4 You should see something like that.

Image classification (persons, animals, other) on raspberry pi from pi-camera (in live time) using custom model tflite (output to terminal) (Photo,GIF by Author) https://github.com/oleksandr-g-rock/image_classification_pi_camera_live_time_custom_model_tflite/blob/main/0_3rpJEUsVESUEAJgP.gif

Result:

In this article, we created a solution for image classification for the classes (persons, animals, others) on Raspberry Pi from Pi-camera in live time.

I will use a custom model tflite which I converted from h5.

And all results will be shown in the terminal. Code for this article is available here.
['Alex G.']
['Raspberry Pi', 'Tflite', 'Python', 'Tensorflow Lite', 'TensorFlow']