Original image: https://www.flickr.com/photos/nicolelee/19041780

The input images are directly resized to match the input size of the model. I skipped adding the pad to the input image, it might affect the accuracy of the model if the input image has a different aspect ratio compared to the input size of the model. Always try to get an input size with a ratio close to the input images you will use.

Check the requirements.txt file.

For ONNX, if you have a NVIDIA GPU, then install the onnxruntime-gpu, otherwise use the onnxruntime library.

git clone https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection.git cd ONNX-YOLOv8-Object-Detection pip install -r requirements.txt

For Nvidia GPU computers: pip install onnxruntime-gpu

Otherwise: pip install onnxruntime

Use the Google Colab notebook to convert the model:

You can convert the model using the following code after installing ultralitics (pip install ultralytics):

from ultralytics import YOLO model = YOLO("yolov8m.pt") model.export(format="onnx", imgsz=[480,640])

The original YOLOv8 model can be found in this repository: YOLOv8 Repository

The License of the models is GPL-3.0 license: License

Image inference:

python image_object_detection.py

Webcam inference:

python webcam_object_detection.py

Video inference: https://youtu.be/JShJpg8Mf7M

python video_object_detection.py

YOLOv8 model: https://github.com/ultralytics/ultralytics

YOLOv5 model: https://github.com/ultralytics/yolov5

YOLOv6 model: https://github.com/meituan/YOLOv6

YOLOv7 model: https://github.com/WongKinYiu/yolov7

PINTO0309's model zoo: https://github.com/PINTO0309/PINTO_model_zoo

PINTO0309's model conversion tool: https://github.com/PINTO0309/openvino2tensorflow