Name | onnx-predict-yolov8 JSON |
Version |
1.0.6
JSON |
| download |
home_page | |
Summary | |
upload_time | 2023-06-12 12:55:48 |
maintainer | |
docs_url | None |
author | |
requires_python | <3.12,>=3.8 |
license | MIT License
Copyright (c) 2023 Aalborg University
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
keywords |
onnx
yolov8
onnxruntime
vision
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
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# ONNX-PREDICT-YOLOV8
This repository is a light weight library to ease the use of ONNX models exported by the Ultralytics YOLOv8 framework.
## Example Usage
```python
from onnxruntime import InferenceSession
from PIL import Image
from opyv8 import Predictor
model = Path("path/to/file.onnx")
# List of classes where the index match the class id in the ONNX network
classes = model.parent.joinpath("classes.names").read_text().split("\n")
session = InferenceSession(
model.as_posix(),
providers=[
"CUDAExecutionProvider",
"CPUExecutionProvider",
],
)
predictor = Predictor(session, classes)
img = Image.open("path/to/image.jpg")
print(predictor.predict(img))
```
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