https://github.com/mnjm/yolo11.cpp
Optimized YOLOv11 Inference wrapper with OpenCV's DNN
https://github.com/mnjm/yolo11.cpp
Last synced: about 2 months ago
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Optimized YOLOv11 Inference wrapper with OpenCV's DNN
- Host: GitHub
- URL: https://github.com/mnjm/yolo11.cpp
- Owner: mnjm
- Created: 2025-02-15T14:22:05.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-15T14:39:54.000Z (4 months ago)
- Last Synced: 2025-04-12T15:13:55.234Z (about 2 months ago)
- Language: C++
- Size: 9.77 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# YOLO11.cpp
Lite-weight Optimized C++ wrapper for running YOLO11 object detection using an ONNX model with OpenCV's DNN.
To use it in your project, just include the `./include/yolov11.hpp` and `./src/yolov11.cpp`.
Note: for this to work, place the `coco.names` file in the same directory as the model
CUDA acceleration can be enabled using [`CUDA_ACC`](https://github.com/mnjm/yolo11.cpp/blob/ea0701b79efdde78523e15c5ef5dc021e161c94a/include/yolov11.hpp#L3C12-L3C20) macro. Same with OpenCL using [`OPENCL_ACC`](https://github.com/mnjm/yolo11.cpp/blob/ea0701b79efdde78523e15c5ef5dc021e161c94a/include/yolov11.hpp#L4)
## Example
Running on an Image
```cpp
#include
#include "yolov11.hpp"
#invlude
int main() {
YOLOv11 model("yolo11s.onnx");
cv::Mat img = cv::imread("sample.jpg");
std::vector bbox_list = model.detect(img);
for (auto& bbox : bbox_list) {
std::cout << "Label:" << bbox.label << " Conf: " << bbox.conf;
std::cout << "(" << bbox.x1 << ", " << bbox.y1 << ") ";
std::cout << "(" << bbox.x2 << ", " << bbox.y2 << ")" << std::endl;
bbox.draw(img);
}
cv::imwrite("sample_out.jpg", img);
return 0;
}
```
## Class Targeted NMS
You can pass a function or callable to filter valid classes, making NMS slightly more efficient.
```cpp
YOLOv11 model("yolo11s.onnx", 0.45f, 0.45f,
[](int lbl_id, const std::string& lbl) {
return lbl_id >= 0 && lbl_id <= 8;/* Only vehicles */
}
);
```
(or)
```cpp
std::map valid_class_d = {
{1, "bicycle"},
{2, "car"},
{3, "motorcycle"},
{4, "airplane"},
{5, "bus"},
{6, "train"},
{7, "truck"},
{8, "boat"},
};
YOLOv11 model("yolo11s.onnx", 0.45f, 0.45f,
[](int lbl_id, const std::string& lbl) {
return valid_class_d.find(lbl_id) != valid_class_d.end();
}
);
```
To get all `{class_id, name}` pairs:
```cpp
for (const auto& [id, name] : model.getClassIdNamePairs()) {
std::cout << id << ": " << name << std::endl;
}
```