It is a two step process:

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

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

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

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/