mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
870551f3f5
* Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add comments to create API docs * Improve OCR comments * fix conflict * Fix OCR Readme * Fix PPOCR readme * Fix PPOCR readme * fix conflict * Improve ascend readme * Improve ascend readme * Improve ascend readme * Improve ascend readme
88 lines
4.2 KiB
Markdown
Executable File
88 lines
4.2 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
|
|
# YOLOv5 Python Deployment Example
|
|
|
|
Before deployment, two steps require confirmation
|
|
|
|
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
|
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
|
|
|
This directory provides examples that `infer.py` fast finishes the deployment of YOLOv5 on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
|
|
|
|
```bash
|
|
# Download the example code for deployment
|
|
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
|
cd examples/vision/detection/yolov5/python/
|
|
|
|
# Download yolov5 model files and test images
|
|
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_infer.tar
|
|
tar -xf yolov5s_infer.tar
|
|
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
|
|
|
# CPU inference
|
|
python infer.py --model yolov5s_infer --image 000000014439.jpg --device cpu
|
|
# GPU inference
|
|
python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu
|
|
# TensorRT inference on GPU
|
|
python infer.py --model yolov5s_infer --image 000000014439.jpg --device gpu --use_trt True
|
|
# KunlunXin XPU inference
|
|
python infer.py --model yolov5s_infer --image 000000014439.jpg --device kunlunxin
|
|
# Huawei Ascend Inference
|
|
python infer.py --model yolov5s_infer --image 000000014439.jpg --device ascend
|
|
```
|
|
|
|
The visualized result after running is as follows
|
|
|
|
<img width="640" src="https://user-images.githubusercontent.com/67993288/184309358-d803347a-8981-44b6-b589-4608021ad0f4.jpg">
|
|
|
|
## YOLOv5 Python Interface
|
|
|
|
```python
|
|
fastdeploy.vision.detection.YOLOv5(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
|
|
```
|
|
|
|
YOLOv5 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
|
|
|
|
> ```python
|
|
> YOLOv5.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](../../../../../docs/api/vision_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 `stris_mini_padide` member variable. Default `stride=32`
|
|
|
|
|
|
|
|
## Other Documents
|
|
|
|
- [YOLOv5 Model Description](..)
|
|
- [YOLOv5 C++ Deployment](../cpp)
|
|
- [Model Prediction Results](../../../../../docs/api/vision_results/)
|
|
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
|