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.

Installation

git clone https://github.com/SihabSahariar/Yolov11-ONNX-Object-Detection.git
cd Yolov11-ONNX-Object-Detection
pip install -r requirements.txt

ONNX Runtime

For Nvidia GPU computers: pip install onnxruntime-gpu

Otherwise: pip install onnxruntime

ONNX model

Use the Google Colab notebook to convert the model:

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

from ultralytics import YOLO

model = YOLO("yolov11n.pt") 
model.export(format="onnx", imgsz=[480,640])

Original YOLOv11 model

The original YOLOv11 model can be found in this repository: YOLOv11 Repository

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

Examples

• Image inference:

python image_object_detection.py

• Webcam inference:

python webcam_object_detection.py

• Video inference:

python video_object_detection.py

References:

• YOLOv11 model: https://github.com/ultralytics/ultralytics
• YOLOv8 model: https://github.com/ultralytics/ultralytics
• PINTO0309's model zoo: https://github.com/PINTO0309/PINTO_model_zoo
• PINTO0309's model conversion tool: https://github.com/PINTO0309/openvino2tensorflow
• ONNX YOLOv8 Object Detection: https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection