FastDeploy/examples/vision/detection/yolov6/python/README.md

English | 简体中文

YOLOv6 Python Deployment Example

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeployEnvironment Requirements
• 2. Install FastDeploy Python whl package. Refer to FastDeploy Python Installation

This directory provides examples that infer.py fast finishes the deployment of YOLOv6 on CPU/GPU and GPU accelerated by TensorRT. The script is as follows

# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/detection/yolov6/python/

wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_infer.tar
tar -xf yolov6s_infer.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# CPU inference
python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device cpu
# GPU inference
python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device gpu
# KunlunXin XPU inference
python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device kunlunxin
# Huawei Ascend Inference
python infer_paddle_model.py --model yolov6s_infer --image 000000014439.jpg  --device ascend

If you want to verify the inference of ONNX models, refer to the following command:

# Download YOLOv6 model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# CPU inference
python infer.py --model yolov6s.onnx --image 000000014439.jpg --device cpu
# GPU inference
python infer.py --model yolov6s.onnx --image 000000014439.jpg --device gpu
# TensorRT inference on GPU
python infer.py --model yolov6s.onnx --image 000000014439.jpg --device gpu --use_trt True

The visualized result after running is as follows

YOLOv6 Python Interface

fastdeploy.vision.detection.YOLOv6(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

YOLOv6 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, which is the default configuration
• model_format(ModelFormat): Model format. ONNX format by default

predict function

YOLOv6.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)

Model prediction interface. Input images and output detection results.

Parameter

• image_data(np.ndarray): Input data in HWC or BGR format
• conf_threshold(float): Filtering threshold of detection box confidence
• nms_iou_threshold(float): iou threshold during NMS processing

Return

Return fastdeploy.vision.DetectionResult structure. Refer to Vision Model Prediction Results for its description.

Class Member Property

Pre-processing Parameter

Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

• size(list[int]): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
• padding_value(list[float]): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default value [114, 114, 114]
• is_no_pad(bool): Specify whether to resize the image through padding. is_no_pad=True represents no paddling. Default is_no_pad=False
• is_mini_pad(bool): This parameter sets the width and height of the image after resize to the value nearest to the size member variable and to the point where the padded pixel size is divisible by the stride member variable. Default is_mini_pad=False
• stride(int): Used with the is_mini_padide member variable. Default stride=32

Other Documents

• YOLOv6 Model Description
• YOLOv6 C++ Deployment
• Model Prediction Results
• How to switch the model inference backend engine