Kia Ora!
Recently in our AI project, we have been working on monitoring river heights using the Yolo algorithm. It sends an alert like a server when the river height exceeds a certain safe range.
Today I am bringing you a share on how to deploy the Yolo algorithm program using an Android phone

My main development environment is currently an M1 chip MAC, and I often encounter all kinds of bugs on Yolo v8 (but I already have a solution, I'll organize it and update it later), so for today's demo tutorial we're going to start with Yolo v5. I'd like to thank this YouTuber, whose video solved a very large number of problems for me:
https://www.youtube.com/watch?v=zs43IrWTzB0

I won't go over the steps to install Yolo v5, you can easily find their repository on Git Hub.
For labelling and processing the data I used: roboflow
This page is very user-friendly for all versions of Yolo support. There are also many open-source datasets available.

Use the dataset to train:

!python train.py --img 640 ---batch 16 --epochs 10 --data DataSet/data.yaml --weights yolov5s.pt

Validate using the dataset:

!python detect.py --source DataSet/valid/images --weights runs/train/exp/weights/best.pt

After completing the training, we need to export to the tflite format

!python export.py --weights runs/train/exp/weights/best.pt --include tflite

This concludes our training of the model and I can't wait to try it out:

!python detect.py --source 0 --weights runs/train/exp/weights/best.pt

This way we have our own model, and of course we are able to achieve better performance through various manipulations of the dataset during the training process.