Important

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.

Requirements

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

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
```
Original video: https://youtu.be/Snyg0RqpVxY