https://github.com/mnjm/yolo11.py
Wrapper for YOLOv11 ONNX Inference
https://github.com/mnjm/yolo11.py
Last synced: about 2 months ago
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Wrapper for YOLOv11 ONNX Inference
- Host: GitHub
- URL: https://github.com/mnjm/yolo11.py
- Owner: mnjm
- License: agpl-3.0
- Created: 2025-02-08T08:06:40.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-15T04:38:02.000Z (4 months ago)
- Last Synced: 2025-04-12T15:13:55.424Z (about 2 months ago)
- Language: Python
- Size: 44 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YOLO11.py
Python wrapper for running YOLOv11 object detection using an ONNX model with ONNXRuntime, Including optimized post processing with class targeted NMS.
## Usage
```sh
pip install -r requirements.txt
```
If you have GPU, uncomment `onnxruntime` in `requirements.txt` and uncomment `onnxruntime-gpu` line
## Running on Image
```python
from yolo11 import YOLOv11
import cv2
from pathlib import Path
model = YOLOv11("yolo11s.onnx")
test_img = Path("test_imgs/sample.jpg")
img = cv2.imread(str(test_img))
bbox_list = model.detect(img)
for bbox in bbox_list:
img = bbox.draw(img)
cv2.imwrite(str(test_img.parent / test_img.stem) + "_out.jpg", img)
```
## Running on video
You can take a look at `test_vid.py`.
To run it, use
```sh
python3 test_vid.py <--save>
```
`--save` will save the output video.
## Class Targeted NMS
You can pass a function or callable to filter valid classes, making NMS slightly efficient. Example
```python
valid_class_d = {
1: "bicycle",
2: "car",
3: "motorcycle",
4: "airplane",
5: "bus",
6: "train",
7: "truck",
8: "boat",
}
model = YOLOv11(
model_path="yolo11s.onnx",
valid_class_checker=lambda lbl_id, _: 1 <= lbl_id <= 8 # detect only vehicles
# (or)
# valid_class_checker=lambda lbl_id, lbl: lbl_id in valid_class_d and lbl == valid_class_d[lbl_id]
)
```
To get all {class_id, name} pairs
```python
print("\n".join([ f"{k}: {v}" for k,v in model.get_class_id_name_pairs().items() ]))
```