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2.9 KiB
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2.9 KiB
Executable File
English | 简体中文
YOLOv5Cls Python Deployment Example
Before deployment, two steps require confirmation.
-
- Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements.
-
- Install FastDeploy Python whl package. Refer to FastDeploy Python Installation.
This directory provides examples that infer.py fast finishes the deployment of YOLOv5Cls on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
# Download deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/classification/yolov5cls/python/
# Download the YOLOv5Cls model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n-cls.onnx
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# CPU inference
python infer.py --model yolov5n-cls.onnx --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
# GPU inference
python infer.py --model yolov5n-cls.onnx --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
# TensorRT inference on GPU
python infer.py --model yolov5n-cls.onnx --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True
The result returned after running is as follows
ClassifyResult(
label_ids: 265,
scores: 0.196327,
)
YOLOv5Cls Python Interface
fastdeploy.vision.classification.YOLOv5Cls(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
YOLOv5Cls model loading and initialization, among which model_file is the exported ONNX model format
Parameter
- model_file(str): Model file path
- params_file(str): Parameter file path. No need to set when the model is in ONNX format
- runtime_option(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
- model_format(ModelFormat): Model format. ONNX format by default
predict Function
YOLOv5Cls.predict(image_data, topk=1)Model prediction interface. Input images and output classification topk results directly.
Parameter
- input_image(np.ndarray): Input data in HWC or BGR format
- topk(int): Return the topk classification results with the highest prediction probability. Default 1
Return
Return
fastdeploy.vision.ClassifyResultstructure. Refer to Vision Model Prediction Results for the description of the structure.