FURK4NGG
83 lines
3.1 KiB
Markdown
83 lines
3.1 KiB
Markdown
## 👀 cv-model-pt_to_hef Overview
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<h1 align="center">Convert Computer Vision model .pt to .hef for Rasberry Pi 5 Hailo AI HAT</h1>
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## 🔎 Preparation
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<details>
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<summary>1. Prepare docker enviroment </summary>
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Follow these steps:
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```bash
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sudo apt update
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sudo apt upgrade -y
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sudo apt install -y apt-transport-https ca-certificates curl software-properties-common
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curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
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echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
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sudo apt update
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sudo apt install -y docker-ce docker-ce-cli containerd.io
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sudo systemctl start docker
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sudo systemctl enable docker
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docker --version
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sudo docker run hello-world
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sudo systemctl stop docker.socket
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sudo systemctl stop docker.service
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sudo systemctl status docker
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sudo systemctl status docker.socket
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sudo mv /var/lib/docker /home/$USER/docker_data
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sudo ln -s /home/$USER/docker_data /var/lib/docker
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sudo systemctl start docker
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sudo systemctl enable docker
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Docker Root Dir: /home/$USER/docker_data
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```
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</details>
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2. `Go to the`[`HAILO AI DEVELOPER ZONE`](https://hailo.ai/developer-zone/software-downloads/)`adress 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]`
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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.`
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<details>
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<summary>3. Get your .pt model and convert it to .onnx</summary>
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1. Run this .py code at the same directory with your .pt model:
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```bash
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!pip install ultralytics
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from ultralytics import YOLO
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model = YOLO("model.pt")
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model.export(format="onnx")
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```
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</details>
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## 📦 Setup
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1. ``` bash`unzip hailo8_ai_sw_suite_2025-10_docker.zip -d /home/$USER/docker_hailo` ```
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2. `cd /home/$USER/docker_hailo/`
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3. `Edit your .sh document and delete these lines:`
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> -v /etc/timezone:/etc/timezone:ro \
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> -v /etc/localtime:/etc/localtime:ro`
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4. `./hailo_ai_sw_suite_docker_run.sh --override`
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> *If you want to continue with your already configured project:*./hailo_ai_sw_suite_docker_run.sh --resume
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5. /home/$USER/docker_hailo/shared_with_docker/train/images/(60%–80% of your photos)
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/home/$USER/docker_hailo/shared_with_docker/models/model.onnx
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6. `git clone https://github.com/LukeDitria/RasPi_YOLO.git`
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7. `cd RasPi_YOLO/` Then you shold be in '/local/workspace/RasPi_YOLO/' directory
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8. `python hailo_calibration_data.py --data_dir /local/shared_with_docker/train/images/ --target_dir /local/shared_with_docker/doc`
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9. `hailomz compile --ckpt /local/shared_with_docker/models/model.onnx --calib-path /local/shared_with_docker/doc/calib/ --yaml /local/workspace/hailo_model_zoo/hailo_model_zoo/cfg/networks/yolov11n.yaml --classes 2 --hw-arch hailo8`
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## 🎉 Run
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``
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