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Python scripts performing object detection using the YOLOv8 model in ONNX.
| Date | Stars |
|---|---|
| 2026-07-24 | 490 |
| 2026-07-25 | 490 |
| 2026-07-28 | 490 |
| 2026-07-30 | 490 |
| 2026-08-06 | 490 |
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*Original image: [https://www.flickr.com/photos/nicolelee/19041780](https://www.flickr.com/photos/nicolelee/19041780)*
# 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
```shell
git clone https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection.git
cd ONNX-YOLOv8-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: [](https://colab.research.google.com/drive/1-yZg6hFg27uCPSycRCRtyezHhq_VAHxQ?usp=sharing)
You can convert the model using the following code after installing ultralitics (`pip install ultralytics`):
```python
from ultralytics import YOLO
model = YOLO("yolov8m.pt")
model.export(format="onnx", imgsz=[480,640])
```
[//]: # (The original models were converted to different formats (including .onnx) by [PINTO0309](https://github.com/PINTO0309). Download the models from **[his repository]**(https://github.com/PINTO0309/PINTO_model_zoo/tree/main/345_YOLOv8). For that, you can either run the `download_single_batch.sh` or copy the download link inside that script in your browser to manually download the file. Then, extract and copy the downloaded onnx models (for example `yolov8m_480x640.onnx`) to your **[models directory](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection/tree/main/models)**, and fix the file name in the python scripts accordingly.)
# Original YOLOv8 model
The original YOLOv8 model can be found in this repository: [YOLOv8 Repository](https://github.com/ultralytics/ultralytics)
- The License of the models is GPL-3.0 license: [License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)
# Examples
* **Image inference**:
```shell
python image_object_detection.py
```
* **Webcam inference**:
```shell
python webcam_object_detection.py
```
* **Video inference**: https://youtu.be/JShJpg8Mf7M
```shell
python video_object_detection.py
```
*Original video: [https://youtu.be/Snyg0RqpVxY](https://youtu.be/Snyg0RqpVxY)*
# References:
* YOLOv8 model: [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)
* YOLOv5 model: [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
* YOLOv6 model: [https://github.com/meituan/YOLOv6](https://github.com/meituan/YOLOv6)
* YOLOv7 model: [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7)
* PINTO0309's model zoo: [https://github.com/PINTO0309/PINTO_model_zoo](https://github.com/PINTO0309/PINTO_model_zoo)
* PINTO0309's model conversion tool: [https://github.com/PINTO0309/openvino2tensorflow](https://github.com/PINTO0309/openvino2tensorflow)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0a484e0a95bc1130, topic:computer-vision, topic:object-detection, topic:yolo