Converting a YOLOv8 Image Detection Model from .pt to .h5, .weights or .cfg Format

Question

How can I convert a trained YOLOv8 image detection model saved as .pt extension to either .h5, .weights or .cfg file format?

I want to know if is it possible to do it and if possible how?

Answer 1

Here are the Steps to convert yolo.pt model into yolo.weights

1. Clone YOLO Repository: Clone the official YOLO repository, which includes the Darknet framework and conversion scripts:

   git clone https://github.com/AlexeyAB/darknet
   cd darknet
   

2. Configure Darknet: Make sure you have Darknet configured to build with CUDA and OpenCV support by editing the Makefile as needed. Refer to the Darknet documentation for instructions specific to your system.

3. Convert PyTorch Model: You can use a script like export.py from the YOLOv5 repository to convert the PyTorch model to the Darknet format. This script can be found in the YOLOv5 repository:

   git clone https://github.com/ultralytics/yolov5
   cd yolov5
   python export.py --weights /path/to/your/yolov8.pt --img-size 640 --dynamic --simplify
   

   Replace /path/to/your/yolov8.pt with the path to your YOLOv8 PyTorch model. You can also adjust the --img-size parameter based on your requirements.

4. Find the Converted Weights: The script will create a yolov8-exported.onnx file in the yolov5 repository directory. You can then convert this ONNX file to Darknet format using the onnx2darknet.py script, which is also available in the YOLOv5 repository.

   python export.py --weights /path/to/your/yolov8-exported.onnx
   

   This command will create a yolov8.weights file, which is the Darknet format model.