算法部署-使用Python+ONNXRuntime部署YOLOv9目标检测算法-优质算法部署项目实战.zip

# YOLOv9 with ONNX & ONNXRuntime Performing Object Detection for YOLOv9 with ONNX and ONNXRuntime  ## 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 ```shell cd 项目 pip install -r requirements.txt ``` ### ONNX Runtime For Nvidia GPU computers: `pip install onnxruntime-gpu` Otherwise: `pip install onnxruntime` ## ONNX model and Class metadata You can download the onnx model and class metadata file on the link below ``` https://drive.google.com/drive/folders/1QH5RCF5WOk53SfdzsHTFkXAdzMLbbQeO?usp=sharing ``` ## Examples ### Arguments List the arguments available in main.py file. - `--source`: Path to image or video file - `--weights`: Path to yolov9 onnx file (ex: weights/yolov9-c.onnx) - `--classes`: Path to yaml file that contains the list of class from model (ex: weights/metadata.yaml) - `--score-threshold`: Score threshold for inference, range from 0 - 1 - `--conf-threshold`: Confidence threshold for inference, range from 0 - 1 - `--iou-threshold`: IOU threshold for inference, range from 0 - 1 - `--image`: Image inference mode - `--video`: Video inference mode - `--show`: Show result on pop-up window - `--device`: Device use for inference, default = cpu. Note: If you want to use `cuda` for inference, please make sure you are already install `onnxruntime-gpu` before running the script. This code provides two modes of inference, image and video inference. Basically, you just add `--image` flag for image inference and `--video` flag for video inference when you are running the python script. If you have your own custom model, don't forget to provide a yaml file that consists the list of class that your model want to predict. This is example of yaml content for defining your own classes: ``` names: 0: person 1: bicycle 2: car 3: motorcycle 4: airplane . . . . n: object ``` ### Inference on Image ``` python main.py --source assets/sample_image.jpeg --weights weights/yolov9-c.onnx --classes weights/metadata.yaml --image ``` ### Inference on Video ``` python main.py --source assets/road.mp4 --weights weights/yolov9-c.onnx --classes weights/metadata.yaml --video ```