https://github.com/developer239/yolo-cmake-opencv-onnx-cpp
Use YOLO with your favorite language and computer vision library. This example includes ONNX runtime for better weights support.
https://github.com/developer239/yolo-cmake-opencv-onnx-cpp
cmake cpp macos onnx onnxruntime opencv windows yolov5 yolov7
Last synced: 12 months ago
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Use YOLO with your favorite language and computer vision library. This example includes ONNX runtime for better weights support.
- Host: GitHub
- URL: https://github.com/developer239/yolo-cmake-opencv-onnx-cpp
- Owner: developer239
- Created: 2022-09-29T13:05:39.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-06-15T16:24:18.000Z (almost 3 years ago)
- Last Synced: 2025-04-10T22:53:24.667Z (12 months ago)
- Topics: cmake, cpp, macos, onnx, onnxruntime, opencv, windows, yolov5, yolov7
- Language: C++
- Homepage:
- Size: 86.9 MB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# YOLO CMake OpenCV ONNX CPP
Object detection implementation is inspired
by [hpc203/yolov7-opencv-onnxrun-cpp-py](https://github.com/hpc203/yolov7-opencv-onnxrun-cpp-py)
This repository serves mostly as documentation for my personal use, demonstrating how to use ONNX and OpenCV with CMake
on MacOS.
## MacOS installation
1) Install dependencies:
```bash
$ brew install cmake
$ brew install onnxruntime
$ brew install opencv
```
2) If you are on ARM then you are good to go 🎉
3) **If you are not using an ARM processor**, [download correct runtime 1.12.1 header files from the official GitHub repository](https://github.com/microsoft/onnxruntime/releases/tag/v1.12.1).
4) Put onnxruntime files to `external/`
5) Set `ONNXRUNTIME_ROOT` env variable to your onnxruntime folder name
## Windows installation
1) Install dependencies:
```bash
$ choco install cmake --pre
$ choco install opencv
```
2) Download [ONNX Runtime 1.12.1](https://github.com/microsoft/onnxruntime/releases/download/v1.12.1/onnxruntime-win-x64-1.12.1.zip)
3) Extract and put onnxruntime folder to `external/onnxruntime-win-x64-1.12.1`
## Model
- This example utilizes a custom-trained model. If you wish to train your own model, you can create a dataset
using [Roboflow](https://roboflow.com/#annotate) and then use one of
their [official notebooks](https://github.com/roboflow/notebooks) to perform the training. There are also many
comprehensive tutorials in the official [ultralytics/yolov5 repository](https://github.com/ultralytics/yolov5)
- You are free to use any model you prefer, but be sure to update the `class.names` file accordingly. Additionally, for instance segmentation, it is important to pass `SegNetConfig` with proper parameters.
## Preview
100 epochs. Trained on YOLOv5n-640 models. Inference on M1 CPU takes ~40ms.
**Object detection dataset information:**
- 186 training images
- 57 validation images
- 25 test images
**Instance segmentation dataset information:**
- 540 training images (AUGMENTATION rotation: Between -15° and +15°)
- 53 validation images
- 28 test images
