Yolov5 export to CPU

It is a two step process:

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

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/karanjakhar/yolov5.git
cd yolov5
pip3 install tensorflow==2.3.1
pip install -r requirements.txt
python3 models/tf.py --weight yolov5s.pt --cfg models/yolov5s.yaml --img 416 

Run the inference on CPU

If you have created conda environment in conversion step then activate it ( conda activate yolov5_env ) and follow below steps. Otherwise I recommend creating a conda environment:

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

then follow below steps:

git clone https://github.com/karanjakhar/yolov5-export-to-cpu.git
cd yolov5-export-to-cpu
pip3 install -r requirements.txt
python3 yolov5_tflite_folder_of_images_inference.py --weights yolov5s-fp16.tflite --folder_path images/ 

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/