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

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FastestDet Python Deployment Example

Before deployment, two steps require confirmation

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

This directory provides examples that infer.py fast finishes the deployment of FastestDet 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/fastestdet/python/

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

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

The visualized result after running is as follows

FastestDet Python Interface

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

FastestDet 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

FastestDet.predict(image_data)

Model prediction interface. Input images and output detection results.

Parameter

• image_data(np.ndarray): Input data in HWC or BGR format

Return

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

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 [352, 352]

Other Documents

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