Yolov5 export to Raspberry Pi

It is a two step process:

1. Convert model weights to tflite
2. Run the inference on Raspberry Pi

Convert Model Weights to tflite

If you don't want to install anything on your system then use this Google Colab (Recommended).

And if you want to perform the conversion on your system then follow bellow instructions:

I recommend create a new conda environment for this as we need python==3.7.0 for this:

conda create -n yolov5_env python==3.7.0
conda activate yolov5_env

Then run below commands and replace yolov5s.pt with your own model path and also change yolov5s.yaml accordingly.

git clone https://github.com/ultralytics/yolov5.git
cd yolov5
pip3 install tensorflow==2.3.1
pip install -r requirements.txt
python3 export.py --weights yolov5s.pt --img 418 --batch 1 --include onnx tflite

Run the inference on Raspberry Pi

Just clone this repository and we are good to go:

git clone https://github.com/karanjakhar/yolov5-export-to-raspberry-pi.git
cd yolov5-export-to-raspberry-pi
chmod +x setup.sh
./setup.sh
python3 yolov5_tflite_folder_of_images_inference.py --weights yolov5s-fp16.tflite --folder_path images/ 

Move your own model tflite file to raspberry pi and use that with above command.

yolov5_tflite_inference.py this file contains main inference code which you can use with your own project.

Other files show examples how to use it. I have placed a tflite file and some sample images to run a quick test.

To Run Examples:

For webcam:

python3 yolov5_tflite_webcam_inference.py -w yolov5s-fp16.tflite

For image:

python3 yolov5_tflite_image_inference.py -w yolov5s-fp16.tflite -i images/bus.jpg

For video:

python3 yolov5_tflite_video_inference.py -w yolov5s-fp16.tflite -v <your video path>

For folder of images:

python3 yolov5_tflite_folder_of_images_inference.py -w yolov5s-fp16.tflite -f images/