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README.md

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NanoDetPlus Python Deployment Example

Before deployment, two steps require confirmation

This directory provides examples that infer.py fast finishes the deployment of NanoDetPlus 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/nanodet_plus/python/

# Download NanoDetPlus model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/nanodet-plus-m_320.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# CPU inference
python infer.py --model nanodet-plus-m_320.onnx --image 000000014439.jpg --device cpu
# GPU inference
python infer.py --model nanodet-plus-m_320.onnx --image 000000014439.jpg --device gpu
# TensorRT inference on GPU 
python infer.py --model nanodet-plus-m_320.onnx --image 000000014439.jpg --device gpu --use_trt True

The visualized result after running is as follows

NanoDetPlus Python Interface

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

NanoDetPlus 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

NanoDetPlus.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 [320, 320]
  • 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 [0, 0, 0]
  • keep_ratio(bool): Whether to keep the aspect ratio unchanged during resize. Default false
  • reg_max(int): The reg_max parameter in GFL regression. Default 7.
  • downsample_strides(list[int]): This parameter is used to change the down-sampling multiple of the feature map that generates anchor, containing four integer elements that represent the default down-sampling multiple for generating anchor. Default [8, 16, 32, 64]

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