Add yolov8 ncnn example

ReadMe Convert yolov8 model to ncnn model workflow:

step 1:

If you don't want to train the model yourself. You should go to the ultralytics website download the pretrained model file. original pretrained model from https://docs.ultralytics.com/models/yolov8/#supported-tasks-and-modes

step 2:

run this command.

conda create --name yolov8 python=3.11
conda activate yolov8
pip install ultralytics onnx numpy protobuf

step 3:

save source code file(export_model_to_ncnn.py):

from ultralytics import YOLO
detection_models = [
    ["./Detection-pt/yolov8n.pt", "./Detection-pt/"],
    ["./Detection-pt/yolov8s.pt", "./Detection-pt/"],
    ["./Detection-pt/yolov8m.pt", "./Detection-pt/"],
    ["./Detection-pt/yolov8l.pt", "./Detection-pt/"],
    ["./Detection-pt/yolov8x.pt", "./Detection-pt/"]
]
for model_dict in detection_models:
    model = YOLO(model_dict[0])  # load an official pretrained weight model
    model.export(format="ncnn", dynamic=True, save_dir=model_dict[1], simplify=True)

step 4:

run command:

python export_model_to_ncnn.py

Test Image:

input image(640x640): 640x640

output image(640x640): output

Jun 12 '24 07:06 whyb

Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.
1 out of 2 committers have signed the CLA.

:white_check_mark: nihui
:x: whyb
_{You have signed the CLA already but the status is still pending? Let us recheck it.}