👀 cv-model-pt_to_hef Overview

Convert Computer Vision model .pt to .hef for Rasberry Pi 5 Hailo AI HAT

🔎 Preparation

1. Prepare docker enviroment

Follow these steps:

sudo systemctl stop docker.socket
sudo systemctl stop docker.service

sudo systemctl status docker
sudo systemctl status docker.socket

sudo mv /var/lib/docker /home/$USER/docker_data
sudo ln -s /home/$USER/docker_data /var/lib/docker

sudo systemctl start docker
sudo systemctl enable docker

Docker Root Dir: /home/$USER/docker_data

2. Go to theHAILO AI DEVELOPER ZONEadress and download .zip file with this configration: Software Package[AI Software Suite], Software Sub-Package[AI Software Suite], Architecture[x86], OS[Linux], Python Version[3.8]
3. In model training, place 60%–80% of the photos you use into the train/images folder. The photos should be raw, unlabelled images — there should be no labeling process applied to them. You don’t need labels or a classes.txt file; only the images folder is required.

3. Get your .pt model and convert it to .onnx

1. Run this .py code at the same directory with your .pt model:

!pip install ultralytics
from ultralytics import YOLO

model = YOLO("model.pt")
model.export(format="onnx")

/home/$USER/docker_hailo/hailo8_ai_sw_suite_2025-10_docker.zip
/home/$USER/docker_hailo/train/images/(60%–80% of your photos)
/home/$USER/docker_hailo/shared_with_docker/models/model.onnx
/home/bob/Docker_hailo/shared_with_docker/doc/

📦 Setup

1. unzip hailo8_ai_sw_suite_2025-10_docker.zip -d /home/$USER/docker_hailo
2. Edit your .sh document and delete these lines:

   -v /etc/timezone:/etc/timezone:ro
   -v /etc/localtime:/etc/localtime:ro`

3. ./hailo_ai_sw_suite_docker_run.sh --override

🎉 Run

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