https://github.com/ibaiGorordo/ONNX-YOLOv6-Object-Detection
Python scripts performing object detection using the YOLOv6 model in ONNX.
https://github.com/ibaiGorordo/ONNX-YOLOv6-Object-Detection
Last synced: 7 months ago
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Python scripts performing object detection using the YOLOv6 model in ONNX.
- Host: GitHub
- URL: https://github.com/ibaiGorordo/ONNX-YOLOv6-Object-Detection
- Owner: ibaiGorordo
- License: mit
- Created: 2022-06-25T11:32:25.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-13T03:48:20.000Z (almost 3 years ago)
- Last Synced: 2024-11-09T15:42:31.847Z (12 months ago)
- Language: Python
- Size: 16.9 MB
- Stars: 93
- Watchers: 4
- Forks: 23
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ONNX YOLOv6 Object Detection
Python scripts performing object detection using the YOLOv6 model in ONNX.
*Original image: https://commons.wikimedia.org/wiki/File:Motorcyclists_lane_splitting_in_Bangkok,_Thailand.jpg*
# Important
- About the YOLOv6 name: https://github.com/meituan/YOLOv6/blob/main/docs/About_naming_yolov6.md
- 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.
- The original YOLOv6 repository is still in development, so things might not work in the future.
# 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/ibaiGorordo/ONNX-YOLOv6-Object-Detection.git
cd ONNX-YOLOv6-Object-Detection
pip install -r requirements.txt
```
### ONNX Runtime
For Nvidia GPU computers:
`pip install onnxruntime-gpu`
Otherwise:
`pip install onnxruntime`
# ONNX model
~~The original model was converted to ONNX using the following Colab notebook from the original repository, run the notebook and save the download model into the [models folder](https://github.com/ibaiGorordo/ONNX-YOLOv6-Object-Detection/tree/main/models):~~
- ~~**Convert YOLOv6 ONNX for Inference** [](https://colab.research.google.com/drive/1pke1ffMeI2dXkIAbzp6IHWdQ0u8S6I0n?usp=sharing)~~
You can find the ONNX models in the Assets section of the official repository Releases (e.g. [Release 2.1](https://github.com/meituan/YOLOv6/releases/tag/0.2.1)). Download the ONNX file and save the download model into the [models folder](https://github.com/ibaiGorordo/ONNX-YOLOv6-Object-Detection/tree/main/models):
- Additionally, if you want to convert the model to ONNX yourself, you can follow the instructions in: https://github.com/meituan/YOLOv6/tree/main/deploy/ONNX
- The License of the models is GPL-3.0 license: [License](https://github.com/meituan/YOLOv6/blob/main/LICENSE)
# Pytorch model
The original Pytorch model can be found in this repository: [YOLOv6 Repository](https://github.com/meituan/YOLOv6)
# Examples
* **Image inference**:
```
python image_object_detection.py
```
* **Webcam inference**:
```
python webcam_object_detection.py
```
* **Video inference**: https://youtu.be/yYo0XQp97vo
```
python video_object_detection.py
```
*Original video: https://youtu.be/yXEb0fWLJIY*
* **Comparison with YOLOv5**: https://youtu.be/yd1DhOf8mW4
```
python comparison_with_yolov5.py
```
Convert YOLOv5 model to ONNX: [](https://colab.research.google.com/drive/1V-F3erKkPun-vNn28BoOc6ENKmfo8kDh?usp=sharing)
# References:
* YOLOv6 model: https://github.com/meituan/YOLOv6
* YOLOv5 model: https://github.com/ultralytics/yolov5